{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"bank_analysis.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"-5NhaE3lHWqy"},"source":["# 0.Problem Statement"]},{"cell_type":"markdown","metadata":{"id":"gaFG_y0QHjUB"},"source":["This dataset is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product(bank term deposit) would be ('yes') or not('no') subscribed.\n","\n","Analysis outcome:\n","- Dataset exploration\n","- Feature engineering and selection\n","- Training and predicting\n","- Evaluating model performances\n","- Summary of project achievements\n","- Marketing insights and future improvements\n","\n","Objective:\n","- The classification goal is training a model to predict whether a client will subscribe a term deposit (yes or no at label 'y')."]},{"cell_type":"markdown","metadata":{"id":"Hubrs58QLXGA"},"source":["# 1.Dataset exploration\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"v4fpYsASHihI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632118308516,"user_tz":-480,"elapsed":4826,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"7f196024-462d-49b7-fd2a-9979efaaf1cb"},"source":["pip install pandas_summary"],"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting pandas_summary\n","  Downloading pandas_summary-0.0.7-py2.py3-none-any.whl (5.2 kB)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from pandas_summary) (1.1.5)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pandas_summary) (1.19.5)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->pandas_summary) (2018.9)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->pandas_summary) (2.8.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->pandas_summary) (1.15.0)\n","Installing collected packages: pandas-summary\n","Successfully installed pandas-summary-0.0.7\n"]}]},{"cell_type":"markdown","metadata":{"id":"j77azNqXLlPn"},"source":["For ease of operation, we have set up to read and write files from google drive. We have also set up a mirror of the project on github for standby."]},{"cell_type":"code","metadata":{"id":"yV4xhpDMR5-m","executionInfo":{"status":"ok","timestamp":1632118309930,"user_tz":-480,"elapsed":327,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["# [Optional if read/write files on other plantforms] Operations to mount the google drive\n","# import os\n","# from google.colab import drive\n","# drive.mount('/content/drive')\n","\n","# path = \"/content/drive/MyDrive/Python_workspace/DSA5101/bank_analysis\"\n","\n","# os.chdir(path)\n","# os.listdir(path)"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"VZeAckYVLg8d","executionInfo":{"status":"ok","timestamp":1632118327479,"user_tz":-480,"elapsed":1021,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["# import packages and raw data\n","import pandas as pd\n","import numpy as np\n","import matplotlib\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from pandas_summary import DataFrameSummary\n","\n","import xgboost as xgb\n","import operator\n","\n","raw_df = pd.read_csv('https://raw.githubusercontent.com/shimmerjordan/DSML_project_bank_classifying/main/bank-full.csv',sep=';')\n","# raw_df = pd.read_csv('bank-full.csv',sep=';')"],"execution_count":4,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"2wZZsoDJS9f9"},"source":["## 1.1 Data Overall Overview"]},{"cell_type":"code","metadata":{"id":"LkAVqk0yTEri","colab":{"base_uri":"https://localhost:8080/","height":223},"executionInfo":{"status":"ok","timestamp":1631972575066,"user_tz":-480,"elapsed":295,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"6eaf8e0a-1ce1-46f3-db0e-1a80a363e728"},"source":["dfs_overview = DataFrameSummary(raw_df)\n","dfs_overview.columns_stats"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>job</th>\n","      <th>marital</th>\n","      <th>education</th>\n","      <th>default</th>\n","      <th>balance</th>\n","      <th>housing</th>\n","      <th>loan</th>\n","      <th>contact</th>\n","      <th>day</th>\n","      <th>month</th>\n","      <th>duration</th>\n","      <th>campaign</th>\n","      <th>pdays</th>\n","      <th>previous</th>\n","      <th>poutcome</th>\n","      <th>y</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>counts</th>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","      <td>45211</td>\n","    </tr>\n","    <tr>\n","      <th>uniques</th>\n","      <td>77</td>\n","      <td>12</td>\n","      <td>3</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>7168</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>31</td>\n","      <td>12</td>\n","      <td>1573</td>\n","      <td>48</td>\n","      <td>559</td>\n","      <td>41</td>\n","      <td>4</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>missing</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>missing_perc</th>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","      <td>0%</td>\n","    </tr>\n","    <tr>\n","      <th>types</th>\n","      <td>numeric</td>\n","      <td>categorical</td>\n","      <td>categorical</td>\n","      <td>categorical</td>\n","      <td>bool</td>\n","      <td>numeric</td>\n","      <td>bool</td>\n","      <td>bool</td>\n","      <td>categorical</td>\n","      <td>numeric</td>\n","      <td>categorical</td>\n","      <td>numeric</td>\n","      <td>numeric</td>\n","      <td>numeric</td>\n","      <td>numeric</td>\n","      <td>categorical</td>\n","      <td>bool</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  age          job      marital  ... previous     poutcome      y\n","counts          45211        45211        45211  ...    45211        45211  45211\n","uniques            77           12            3  ...       41            4      2\n","missing             0            0            0  ...        0            0      0\n","missing_perc       0%           0%           0%  ...       0%           0%     0%\n","types         numeric  categorical  categorical  ...  numeric  categorical   bool\n","\n","[5 rows x 17 columns]"]},"metadata":{},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"-cZEYITEWCwt"},"source":["We can see that none of the data has missing property value and many of the properties have different types, thus we will have to do numerical substitutions for some of these attributes which we will discuss in **Feature Engineering**."]},{"cell_type":"code","metadata":{"id":"L27OwqFsWEBi","colab":{"base_uri":"https://localhost:8080/","height":223},"executionInfo":{"status":"ok","timestamp":1631972575066,"user_tz":-480,"elapsed":5,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"39a23f48-d3d4-494c-98a5-1b4e3af3338d"},"source":["raw_df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>job</th>\n","      <th>marital</th>\n","      <th>education</th>\n","      <th>default</th>\n","      <th>balance</th>\n","      <th>housing</th>\n","      <th>loan</th>\n","      <th>contact</th>\n","      <th>day</th>\n","      <th>month</th>\n","      <th>duration</th>\n","      <th>campaign</th>\n","      <th>pdays</th>\n","      <th>previous</th>\n","      <th>poutcome</th>\n","      <th>y</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>58</td>\n","      <td>management</td>\n","      <td>married</td>\n","      <td>tertiary</td>\n","      <td>no</td>\n","      <td>2143</td>\n","      <td>yes</td>\n","      <td>no</td>\n","      <td>unknown</td>\n","      <td>5</td>\n","      <td>may</td>\n","      <td>261</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>unknown</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>44</td>\n","      <td>technician</td>\n","      <td>single</td>\n","      <td>secondary</td>\n","      <td>no</td>\n","      <td>29</td>\n","      <td>yes</td>\n","      <td>no</td>\n","      <td>unknown</td>\n","      <td>5</td>\n","      <td>may</td>\n","      <td>151</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>unknown</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>33</td>\n","      <td>entrepreneur</td>\n","      <td>married</td>\n","      <td>secondary</td>\n","      <td>no</td>\n","      <td>2</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>unknown</td>\n","      <td>5</td>\n","      <td>may</td>\n","      <td>76</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>unknown</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>47</td>\n","      <td>blue-collar</td>\n","      <td>married</td>\n","      <td>unknown</td>\n","      <td>no</td>\n","      <td>1506</td>\n","      <td>yes</td>\n","      <td>no</td>\n","      <td>unknown</td>\n","      <td>5</td>\n","      <td>may</td>\n","      <td>92</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>unknown</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>33</td>\n","      <td>unknown</td>\n","      <td>single</td>\n","      <td>unknown</td>\n","      <td>no</td>\n","      <td>1</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>unknown</td>\n","      <td>5</td>\n","      <td>may</td>\n","      <td>198</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>unknown</td>\n","      <td>no</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   age           job  marital  education  ... pdays  previous poutcome   y\n","0   58    management  married   tertiary  ...    -1         0  unknown  no\n","1   44    technician   single  secondary  ...    -1         0  unknown  no\n","2   33  entrepreneur  married  secondary  ...    -1         0  unknown  no\n","3   47   blue-collar  married    unknown  ...    -1         0  unknown  no\n","4   33       unknown   single    unknown  ...    -1         0  unknown  no\n","\n","[5 rows x 17 columns]"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"JOq7mR5VFgS1"},"source":["## 1.2 Numeric Feature Overview"]},{"cell_type":"markdown","metadata":{"id":"kFdL0mMoKiiK"},"source":["We start by visualising the features in the dataset whose attributes are of the numerical type to investigate their distribution characteristics."]},{"cell_type":"code","metadata":{"id":"3XSKGzLK1PMQ","colab":{"base_uri":"https://localhost:8080/","height":712},"executionInfo":{"status":"ok","timestamp":1631972577429,"user_tz":-480,"elapsed":2367,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"ad1bcdb9-fa6f-4f8d-960d-adaebaff19d9"},"source":["plt.style.use('seaborn-whitegrid')\n","\n","raw_df.hist(bins=30, figsize=(18,12), color='slateblue')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1296x864 with 9 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"_o7Ac_poKPYD"},"source":["## 1.3 Categorical Feature Overview"]},{"cell_type":"markdown","metadata":{"id":"d9CuLq6RLQPD"},"source":["Next we extract the non-numeric types of features and first visualise them for subsequent study."]},{"cell_type":"code","metadata":{"id":"HfsvGSxALNDo"},"source":["categ_columns = ['job','marital','education','default','housing','loan','contact','month','poutcome','y']"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"aTgcyQ1BLbka","colab":{"base_uri":"https://localhost:8080/","height":589},"executionInfo":{"status":"ok","timestamp":1631972579733,"user_tz":-480,"elapsed":2308,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"34ce0607-f792-45fd-ceb2-513f6a025c26"},"source":["plt.figure(figsize=(18,12))\n","for i,col in enumerate(categ_columns,start=1):\n","    plt.subplot(4,4,i)\n","    sns.barplot(x=raw_df[col].value_counts().values, y=raw_df[col].value_counts().index)\n","    plt.title(col)\n","    plt.xlabel('count')\n","plt.tight_layout()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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size 1296x864 with 10 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"N2XTF_RhJEHU"},"source":["## 1.4 Combined or Certain Feature Overview"]},{"cell_type":"markdown","metadata":{"id":"3bWJWlsGNgeC"},"source":["### Date (Day and Month)"]},{"cell_type":"markdown","metadata":{"id":"wOctUWwUOQyS"},"source":["We tried to find a pattern in the distribution of dates, but found that the distribution was evenly distributed, except for January, when the data was slightly less. It is possible that time is not a very important influencing factor, but this also requires the use of algorithms and models in subsequent feature selection to decide on the selection of feature attributes."]},{"cell_type":"code","metadata":{"id":"YozGIMhTJYPr","colab":{"base_uri":"https://localhost:8080/","height":308},"executionInfo":{"status":"ok","timestamp":1631972580832,"user_tz":-480,"elapsed":1108,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"92ce05d8-d4cd-4e90-9db0-a1a0f1c5b833"},"source":["# Day and Month\n","d_m = sns.scatterplot(x='month', y='day', data=raw_df, palette=\"Blues_d\")\n","d_m.set_title(\"Day and Month\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Day and Month')"]},"metadata":{},"execution_count":9},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"3f6YrGRuPxNb"},"source":["Further, we examine the date month with the target category. and further found that there was in fact a significant difference from month to month whether or not they were subscribed. This proves that our previous use of scatter plots to examine the impact of dates was biased. The inner circle in the results indicates the percentage of months with a `'y'` value of `'yes'` in the feature column, while the outer circle shows the percentage of months for the `'no'` group."]},{"cell_type":"code","metadata":{"id":"iJP01JdrPxaa","colab":{"base_uri":"https://localhost:8080/","height":459},"executionInfo":{"status":"ok","timestamp":1631972580832,"user_tz":-480,"elapsed":10,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"2dbf0947-8906-4331-e483-49dc27ebf3c8"},"source":["df_yes = raw_df.loc[raw_df['y']=='yes',]\n","df_no = raw_df.loc[raw_df['y']=='no',]\n","def plt_more_category(df,element):\n","  c = np.unique(np.array(df[element]))\n","  \n","  yes = []\n","  for item in c:\n","    dff = df_yes.loc[df_yes[element]==item,]\n","    yes.append(dff.shape[0])\n","  no = []\n","  for item in c:\n","    dff = df_no.loc[df_no[element]==item,]\n","    no.append(dff.shape[0])\n","\n","  color = sns.hls_palette(len(c), l=.8, s=.9)\n","\n","  t = []\n","  for i in range(len(c)):\n","    t.append('yes')\n","  for i in range(len(c)):\n","    t.append('no')\n","  tt = []\n","  for item in yes:\n","    tt.append(item/df_yes.shape[0])\n","  for item in no:\n","    tt.append(item/df_no.shape[0])\n","\n","  col2 = []\n","  for item in c:\n","    col2.append(item)\n","  for item in c:\n","    col2.append(item)\n","\n","  ttt = pd.DataFrame()\n","  ttt[1]=col2\n","  ttt[2]=tt\n","  ttt[3]=t\n","  \n","  p = []\n","  for i in range(len(c)):\n","    p.append(yes[i]/no[i]-df_yes.shape[0]/df_no.shape[0])\n","\n","  wedges1, texts1, autotexts1 = plt.pie(\n","    no,\n","    autopct = '%3.1f%%',\n","    radius = 2.4,\n","    pctdistance = 1.0,\n","    colors = color,\n","    wedgeprops = {'width': 0.5, 'edgecolor': 'w'}\n","  )\n","\n","  # Inner circle\n","  wedges2, texts2, autotexts2 = plt.pie(\n","    yes,\n","    autopct = '%3.1f%%',\n","    radius = 1.4,\n","    pctdistance = 1.15,\n","    colors = color,\n","    wedgeprops = {'width': 0.5, 'edgecolor': 'w'}\n","  )\n","\n","  plt.legend(wedges1,\n","    c,\n","    fontsize = 12,\n","    title = 'proportion',\n","    loc = 'center right',\n","    bbox_to_anchor = (2.2, 0.6))\n","\n","  # set text properties\n","  plt.setp(autotexts1, size=15, weight='bold')\n","  plt.setp(autotexts2, size=15, weight='bold')\n","  plt.setp(texts1, size=15)\n","\n","  plt.show()\n","  \n","plt_more_category(raw_df,'month')"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"GrSzLGTvUqlw"},"source":["In addition, the histograms allow for more visualisation of the differences in specific values by month. Thus, visualisation tools can be found to have a significant impact on the decision making for profiling."]},{"cell_type":"code","metadata":{"id":"950Yrpc9R8ph","colab":{"base_uri":"https://localhost:8080/","height":293},"executionInfo":{"status":"ok","timestamp":1631972581372,"user_tz":-480,"elapsed":548,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"1ba18c67-2561-461a-96c3-3cbb27310c49"},"source":["sns.countplot(\n","  x='month', \n","  hue=\"y\", \n","  data=raw_df, \n","  palette=\"Blues_d\",\n",")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"pc6MV2d7ObqR"},"source":["### Family Environment(Education & Balance & Marital & Loan)"]},{"cell_type":"markdown","metadata":{"id":"VUzV2Fh1PL_5"},"source":["A person's educational background and marital status can have a significant impact on their savings and therefore on whether or not they will subscribe to a term deposit."]},{"cell_type":"code","metadata":{"id":"YIlecC1cMpej","colab":{"base_uri":"https://localhost:8080/","height":369},"executionInfo":{"status":"ok","timestamp":1631972582968,"user_tz":-480,"elapsed":1598,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"3d059845-bf2c-4da7-e3cf-9e2c7efe5ba5"},"source":["e_b_m_l = sns.catplot(x=\"education\", y=\"balance\", hue=\"marital\", kind='bar', col=\"loan\", data=raw_df)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 793.625x360 with 2 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"nd6v_AwYNFpZ"},"source":["## 1.5 Target Overview"]},{"cell_type":"code","metadata":{"id":"6UfQE7YfNINJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1631972583509,"user_tz":-480,"elapsed":16,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"214c40fd-b7fa-4851-b941-75a9c8e81654"},"source":["dfs_overview['y']"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"no\" count       39922\n","\"no\" perc       88.30%\n","\"yes\" count       5289\n","\"yes\" perc      11.70%\n","counts           45211\n","uniques              2\n","missing              0\n","missing_perc        0%\n","types             bool\n","Name: y, dtype: object"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"VS-pAn5GNhrn","colab":{"base_uri":"https://localhost:8080/","height":349},"executionInfo":{"status":"ok","timestamp":1631972583509,"user_tz":-480,"elapsed":13,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"31a25dfe-928b-41a0-8d95-a89675915cb1"},"source":["# defining labels \n","genus = ['\"yes\" count', '\"no\" count']\n","yes_num = raw_df['y'].value_counts()['yes']\n","no_num = raw_df['y'].value_counts()['no']\n","# portion covered by each label \n","slices = [yes_num, no_num]\n","colors = ['r', 'y']\n","plt.pie(slices, labels=genus, colors=colors,\n","    startangle=110, shadow=True, explode=(0.05, 0.1),\n","    radius=1.0, autopct='%1.1f%%')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["([<matplotlib.patches.Wedge at 0x7f036f7d29d0>,\n","  <matplotlib.patches.Wedge at 0x7f0366f48e10>],\n"," [Text(-0.7553349599474646, 0.8671615179891012, '\"yes\" count'),\n","  Text(0.788175673919586, -0.9048641373385325, '\"no\" count')],\n"," [Text(-0.4269284556224799, 0.490134771037318, '11.7%'),\n","  Text(0.4597691431197584, -0.5278374134474773, '88.3%')])"]},"metadata":{},"execution_count":14},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPQAAADnCAYAAAApbXvLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd3wVdb7/8dfpOSXtpJJOD4QAgVBEQUVQr6jYrqs0C+7dvRb87bX+1tWrrntdvd67+1NX3eKuotjWFRUriqhroUMSegik56Qnp9eZ3x+JkRZIIMmcc/g+Hw8fYEhm3lHemTlzPvMdlSzLMoIgRAW10gEEQRg4otCCEEVEoQUhiohCC0IUEYUWhCgiCi0IUUQUWhCiiCi0IEQRUWhBiCKi0IIQRUShBSGKiEILQhQRhRaEKCIKLQhRRBRaEKKIKLQgRBFRaEGIIqLQghBFRKEFIYqIQgtCFBGFFoQoIgotCFFEFFoQoogotCBEEVFoQYgiotCCEEVEoQUhiohCC0IUEYUWhCgiCj2YZBm++gpuuAGef17pNMIZQCUeJzsIOjqQnnuO0AsvoKup6frY5MmwfbuyuYSoJwo9kGw2vL/5DboXX0Tj8Rz752VlMGHC0OcSzhjilHsgHDyIZ8kSQtnZxDz77PHLDLBy5dDmEs444gh9GuTSUjwPPEDMRx+hlqSTf0FmJlRXg3pof47KskQg0Irf30gg0IwkeZDlILIcQJYlVCo1oEGl0qBWG9HrU9DpUtHpUlCrtUOaVTg9Jyz0M888w/Tp01m9ejW33347WVlZp7yjjRs3smnTJjIzMwG46qqrTnlbffXJJ59w8cUXD/h2pcZGXLfdhvmdd1D39+fhZ5/BvHkDmkeWZbzegzidZbhcpXg8B/D7bfj9TQQCjfj9zUDoFLasQqtNQKdLRa9PRa9Pw2gcg9k8AbO5EJNpLGq1bkC/F+H0RO2PX7/fz0svvTSwhQ6F6Hj4YYxPPUWs13tq23jlldMqtCQFcTg24XBsxeUq7S7xLiTJecrb7J1MMNhOMNiOx7PvmD9VqXSHFXwCcXHTiY+fjUZjHIQsQl+c8Ajd1taG0WjE4XDQ3t7OI488wmuvvQbA888/j9lsZtasWTz66KOoVCrMZjO//e1vMRqN3HPPPTQ3N+P3+7njjjuYNm0aHo8Hna7rJ3psbGzPfnbv3s0jjzyCSqWiqKiI++67j3379vHoo4+iVqt7trtv3z5WrVrF008/DcCMGTPYuHEjS5cuZdasWWzYsIH29nZeeOEF/vznP/Puu++ycOFCHn744dP+D+V6/31Ct95KXF3d6W3IYoHGRjCZ+r5v117a2z+jrW0tHR1fIUmO08swiFQqA/HxZ5GQcAGJifOIi5uGSqVROtYZ44RHaKvVCoDRaCQ1NRW/34/NZiM9PZ0vv/ySP/zhD9x11108+uij5OXlsWrVKlatWsWcOXNob29n1apV2O12vvrqK4xGI0bj8X9yP/bYYzzyyCPk5+dz7733UldXx29+8xvuvfdeJk2axIsvvsjKlSuZMWNGr1ktFgsvv/wyTz31FGvXrmX58uWUlJScdpkDBw/iuOkmrF9/fVrb6eF0wurVsHhxr58SDDppbf2A9vZPaW1dSyBQPzD7HgKy7KOj40s6Or6ksvJBNJp4EhLOxWq9iJSUq9Hr05SOGNX6dcp9+eWX8/HHH3PJJZdgsVhITk6mtLSUBx98EOg6zS0sLGTEiBG4XC7uuece5s+fz4IFC0643UOHDpGfnw/Ak08+CUBFRQWTJk0Cuo7Ezz777AkLXVxcDEB6ejodHR39+baOS5ZlWp54gvj//E+sfv9pb+8IK1ceU2hZDtHS8j4226u0tX2ELJ/iKX2YCYU6aW19n9bW9ykvX0FCwrmkpl5HSso16HSJSseLOv0q9KWXXsodd9yB0Wjk0ksvBbqO3itXrkSlUh3xuW+99Rbbtm1j9erVrF+/nscff7zX7apPctU3EAigVquP2UcwGOz5vUbz42nd6V64d9XW0nbVVWRv3nxa2+nVunXQ0ADDhgHg9/uorNxJbe0NqNXhezp9+kJ0dHxBR8cXlJffQXLyQtLTb8RqvVCclg+Qfr1/YrVaiY+P57333mP+/PkA5Ofn83X36eiHH37I999/z65du1izZg3FxcU8/PDDVFRUnHC7I0eOpKSkBIBf/vKXVFRUMHr0aLZ3T1Zt3ryZCRMmYLFYaGpqAmDv3r24XK7evzG1mlCo/1d2bW+8QXDChMErM0AoBN3XIlpbbTz99C94661naGrKG7x9hhlZ9tHc/BZlZZewYcNwamr+h2Awmn+YDY1+vyF60UUXkZaWhsViAeCBBx7gj3/8I0uWLOGdd95h3LhxZGVl8f7777No0SJuvvlmli9ffsJtPvDAA/z2t7/l+uuvJz4+npEjR/KrX/2K//3f/2XZsmWUlZWxbNky8vPzMZlMXHfddbz33ns9b4EdT0pKCoFAgBUrVvTp+wp4vVQuWkTK4sXEd3b2/T/IqXrlFQBiYkz4fF5SU3MIBM4a/P2GIZ+vhoqKu9mwIYeDB/8vPp9N6UgRq9+DJffddx9XXnklM2fOHKxMQ669tBTfwoWkV1YO7Y5LS6GwkDff/D3V1eUkJqaQnf0cBkPz0OYIMyqVgbS0JeTk3IPJNFbpOBGlz0don8/Htddei8ViiZoyy7LMgZdfRjNr1tCXGXpGQYuKzsPn63r54HBMHPocYUaWfdhsL7Jp0zh27boWj+eQ0pEixhk7+hkMBNh1333kP/sshkBAmRAZGVBTgz8Y4He/W0F8fDIxMW5yc3/PUdf/zmgqlYGsrDvJzf0VWm3syb/gDHZG3pzhdjjYee21FP7+98qVGaC+HtatQ683UFh4Nh0dTQSD8Xg8ecplCkOy7KOm5kk2bhxNff2fkeU+zM2foc64Qrc3NlJ+4YVMfvfd/s9hD4bui2MTJ87qeRvO4ZikZKKwFQg0sn//v7FlyxTa29crHScsnVGFbqyooPG885i0YYPSUX70zjvgcpGRMQKLJR6v143TOQ5Jitox+9PmcpVQUjKXvXuXi7e6jnLGFLqqrIz2iy4if+9epaMcyeWC1atRq9UUF8+js7MFWTbgcuUrnSzs2Wx/ZcuWSXR0/FPpKGHjjCj0ga1bsV9zDfknGXBRTPfV7vz8qciyjCzL4mp3H3m9h9ix4zwqKu5FknxKx1Fc1Be6sqwM+9KlFO7fr3SU3q1bB/X1JCWlk56eg8vVids9kmDQrHSyCCFRU/PfbN06DaezROkwiorqQtfu20fT0qVM2bNH6SgnJknw2muoVCqKi+fhdHYAapxOsf5Yf7hcZWzdOp3a2meVjqKYqC10Q0UFVUuXMr0kQn5id1/tHj16EiqVGkmSsNvF1e7+kmU/Bw7cwd69N52Rp+BRWeimqir23XgjZw/mDRYDrbQUSksxm+MYNWoSnZ0t+P3D8PlSlE4WkWy2l9i+fTY+X+TcSz4Qoq7QrfX1bLjzTuZ8+63SUfqv+yhdVHQuPl/XyqHi4tipczg2s3XrdByOHUpHGTJRVWhnRwdrH3qI+WvXhsfQSH+tWgWhEHl549Dp9AQCfhyOiciymAM9VX5/Hdu3n0NLyxqlowyJqCl0MBBg7bPPMve99zD2ti52uGto6BkFnTjxbDo6mgmF4sQo6GmSJBc7d16Bzfay0lEGXVQUWpZlvvvHPxjz0kuktbQoHef0dJ92FxbOIhT6YRRUnHafPom9e2+moeFvSgcZVFFR6P2bNuH/3e+YEK6DI/2xevVho6AJ3aOg45Eksf716ZPYt2859fV/VjrIoIn4QjdXV1Py619z/pYtSkcZGC4XvPNO9yjoBd2joHoxCjpgZPbv/xl1dS8oHWRQRHSh3Q4HHz31FBd//TWavjyKJlJ0n3aPG1csRkEHhUx5+b9TV/cHpYMMuIgttCzLfPbii0xet444R5TdcdM9Cmq1ppGents9CjqCYNCidLKoUl5+O/X1f1I6xoCK2ELv27ABzyefMHH3bqWjDDxJglWrukdBLxCjoIOovPw22to+VTrGgInIQjvb2/nqxRf5l02biNp3aI8ZBQ2JUdBBIMtBdu26Fqdzp9JRBkTEFVqWZb5atYrizZuJb29XOs7gKSuDkhLM5jhGj55MZ2crfn86Pl+q0smiTihkp6zsUvz+RqWjnLaIK3TFtm10fPwxRWVlSkcZfEeMgroB8Z70YPH5qigru5xQKEKHkrpFVKHdDgfr/vIXLt68OTJHO/vrtdd6RkH1+pjuUdBCMQo6SByOTezdu+y0H6WkpIgptCzL/PONNxhVWkpS89AuRL9fr2deXh6vJiT0fGxlQgIFo0fjOs56u3+Pi2NpVlbPP0WjRgHwRHIy12Vns/Kw7TxvtfKppZer1w0N8Pnn6HR6MQo6RJqb36a6+gmlY5yyiCl03f797F+/nlmlpUO6X7dKxa9TUznL7e752LuxsbRqNKQe9rC8w/2r3c4rtbW8UlvLHa2tXGG341Sp2G8w8EZNDR91Pxu7VaNhe0wMFzlP8LD27tPuCRMOHwUVF8cGU2XlQ9jtkTmoFBGFliSJr157jZkVFRhP9Jd/EOhlmT/X1ZF62IPv5jmd/KK1tU9X2P+QlMStra24NBqs3dvQdZ/SPZuUxB2trSfewOrV4HSSkTGc2NjEw1YFFaOgg0WWA+zZs4hQqPeHIYariCj0ga1baaqsxDFqFO1pQ/vAcC0Qc9RrKksfX2OVGgwMCwRICYVIDAZp1Gr5YVn/Cp0On0rFQb2eB9LSeC+2lydCuN2HjYLOFaOgQ8TjKae8/E6lY/Rb2BdalmW+efNNYsxmbKNH8/HPfsZ3V16JIzH8Hxb+dnw8V9rtAOiBuU4nN2RlsaSjg2eTkvj3tjY+iIvjN42NrImLo9fHynefdufnFyPLkhgFHSI224s0N/9D6Rj9EvaFVqlUzLzySlRqNU2Vlfh9PionTuSD225j8yWX4OntglIY2GgyUXTYvdk3dnTwWm0t8aEQIwIB9LJMQvdpeHwoRLu2l8X1v/gC6upISkpn2LDhOJ1iFHSo7Nv3U7zeWqVj9FnYFxpg/Nlnc9N//zfn/OQnuNrbaa6qIiBJlE+bxvsrVrBj7lz8MTFKxzxCo0aDWZLQH/VxGXgpMZHlbW0khEI0dpe4RaslsZeLbD+MggIUF1+Ay9U1CupwFA5afqFLMNjOvn23KB2jzzQPP/zww0qH6AutTkfG6NFMOO88NFoth3bswN3ZiT42ltbhwymfOhUAa0MD6gG882qnwcBdw4axyWSiLCaGtRYLjVotTyclcVCvZ6PJRIVez9luN79IT+c8lwstUKnXU2I0cvlRN468FxvLiECAiT4fWuCgXs/TSUkUezycc6KVVmw2uPVWYmMT2bjxU0wmC5IUS3z81gH7XoXj83orMJkKMJsLlI5yUhH7OFlHWxtbP/qIki++QKPRkDBsGGq1mhiHgwlff83Ibdui65ZKgO3bYfJk/v73Z6is3ENiYirZ2c9jMDQpnSzq6fWZTJ++F602vF/mRMwR+mgGo5G8iRMZO2MGHoeD6l27CHi9aKxWGsaMoaqwEIPbTXxTU/TcwGE2w0UXodfHUFLyDRZLAmq1H5PpoNLJol4o5ECWA1itFyod5YQi9gh9tObqar59+20OlZQQY7EQl5yMSqUiwWZj0hdfkFlernTE05eeDrW1BKQQv/vdCmJjrcTEeMnL+z0qVVT8bwxrKpWOadN2YTKNVjpKryL2CH00c3w8Y2fOJHfCBJqrq7EdOACAlJJCVWEhthEjiG1rw9zZqXDS0+B0wllnoRk7Foejg8rK3RiNKRiN1eh0UXznWdiQ8HqrSEu7XukgvYqaQkPXW1yxSUkUzJ7NsNGjqd+/n5bqatRaLf7UVA4WFdGakUF8czNGV+RNAQEgy3D11cTEmNm+/Usslq65cIslzB6TG6U8nn3Ex8/BaByudJTjippTboCKivvw+xvIy3sUozGPUChExbZt/PONN3C0thKfmorBZAJZJnfnTiauX09spN1TbTJBYyOSycQzz9yNWq3BZNIxfPhTqNWBk3+9cNri48+hqCg8n0kdFUfo8vISyso+xOl8GKdzK/X1zxMINBEXN43UnDFMnDuXuJQUKktK6GxqQmc04szMpLy4GI/FQmJDAzp/r3Na4SUQgLFjURUVIUlB9u3bitlsRadrwWCI/Bv0I4HPV01CwgXExOQqHeUYEV9ov9/Hq6/+Fkl6ldhYW/dHQzgcm6ire55QyE18wnTSh4+l8PzzMcbGcmjHDpxtbegtFjqysymfNo2gXo+1oQFNb8Md4cThgGXLMJvj2bz5M8zmeGRZT1zc0N6Jdibz+epJT1+idIxjRPwp95YtX/D5539k+vR3UKuPX0at1kpOzv1kZt6ORmPE43Sy47PP2PLRR8iSROKwYWi0WnReL+O/+YaxmzahDYTx6ataDdXVkJnJX//6CJ2d7cTGxpGX93u02ihbATWMTZ26hdjYqUrHOEJEH6F9Pg9vvfU0ubkHiI2t7PXzJMlDe/tn2GwvodFYSEicRs74AgpmzyYUCFBZWorP6USXkEDTyJFUFBWhCQRIsNnCc2UUWYbUVDjnHNRqLTt3fofFkohG48BojJy540gXCLSQmvoTpWMcISJmuXuzZ88WPB47VmvfTjX9/jr27/8ZmzePp6npTcwJCZy3ZAnLHn+cUdOm0VJdTbvNhsdsZsuCBXxw++1UFhYShpXuuQNr1KgfVwUVCx8MrZaW1bhce5SOcYSIPUJLksTq1S+QnFxFUlL/lmANBttobn6b1tb3MRhysaZMZtTUqYycOpXOpiZq9+4lFAyislqpHT+e2nHjMHV2EtfWNkjfzSloboaFC9Hn5NLYWE1jYzV6fRpm81602gh9Sy4CSZKL5OQrlI7RI2ILXV29nw0bPmHMmE3odKc2LOL322hqWkV7+5eYTGOxphb0DKe0VFdj6374nZScTFVhIQ3hNpxiMsFFF2EwGCkp+VqMgirA7d5PZuYK1GqD0lGACL4o9vbbz1Jf/xUTJ749YNtMSrqM4cN/g8VSiCzLVO/axdevv05LbS2xSUmY4uIAyCgvZ9K6dSQ2Kvw20XFHQX3k5f1OjIIOobFj/8KwYcuVjgFE6BG6o6OFjz56idGj9xET0zBg2/V49lNf/0c8ngNYLEUkZ45lwnnnkZKTQ82uXbTW1aHV6/Gkp3Ng6lTsSUkkNjZiUOoB84eNgjqdnRw6tEuMgiogGGxj2LCblY4BRGihN21aS23tbkaO/AKVaqBvkZRxuUqpr38Ov7+RuLhiUnPGUHj++cSnpFBZWnrkcMq0aXhiY5UbTpEkuPpqjEYL27Z1jYLKskqMgg4hn6+atLSl6HRWpaNE3il3IODn97//P6SnN5Cd/e6g70+tNpOVdSc5Ofei1cbj93rZ+dVXbFi9moDPR+KwYWj1ejSBAGM2bmT8t99i8HoHPVcPo7FrFNRs5tln70GlUotRUAXk5v6K4cN/rXSMyDtCV1buZseOf5KXt2tIbuyX5QCdnf+kvv6PAMQnzCBzdH7Xyik6HQcPXzklL48DxcWgUg34yim9CgZhzBhURUWEQiH27t2CxZIkRkGHmNd7iKysO1Ed58ELQyniCv3NN2tob68nN3c9anXo5F8wQCTJS3v759hsf+seTpn+43BKKERlSQneo4dTgsGhGU5xOOCGGzCb49i8+XMxCqqAUMhOQsK5it+FFVGFDgT8rFnzFzIy7CQkKPOXNRRy0Nr6AU1Nr6PTpZCYVMzwiRMZO3MmXqeT6p078Xu9aBMTaRgzhsqJE7tWTmluHryVU6qqYPlyjGkZHDhQisPRjlo9jLi4bajVEXLTSRTQ6ZKwWi9SNENEFbqmZj87dnxNbu4uDAbbyb9gEAWDbbS0/IPW1vcwGHKwphT1DKfYW1qo3bPniOGUmvx8zHY7cSd7UsapkGVIS+seBdWwc+f33aOgTozGmoHfn3BcwaCdzMzbFM0QUaOfu3dvRqNRYzbvUzpKD6dzB2VlC9i+fQ6dnd+Skp3NZStW8JMHHyQ5M5Omykqc7e10pqXx1fXXs/bmm2nMHYTb7o47CioW4x9KbvduvF5lf4BGzBE6GAzy/vt/Ij3dRWLidqXjHMPnq8Zm+yt2+xYslkKS0scx/pxzyBwzhvrycpqqqlBrNARSUzk0eTItmZldK6cM1LO6mpvh8svR5+bR1FRDQ0OVGAVVgMk0ntjYKYrtP2IKXVt7gO3bvyQjowaTqVLpOL3yeMqpr/8Tbvd+YmOLSM4YS8G55x4znOL9YTglOXnghlNMJrj4YrEqqIJUKj2pqdcqtv+IOeU+dGg3KpWamJhIeE0o0dT0Gps25bN//60Eg82MLi5m2eOPc+EttxD0+Xoe61M1YQIf3HYbmxYswN3bA+v66vXXIRQiNzcfvd4gHhCvgPb2z5Ek5RbJiJgj9Bdf/B1JCpGZ+cWQvl11eiQcji3U1z9PKOQkLv7HlVNMcXEc2r4dR2srerO5a+WU4mICBgPWhga0p7JyissFM2eiGZt/1ChojRgFHSKy7MNqvYiYmBxF9h8Rhfb7faxdu4qUlBBW6yal4/Rb13DKNzQ0/AlZlklInEFG93CKVq/n4PbtRw6nTJ2KrFaT2NDQ/6d/hEJwzTVHjYKKVUGHksk0lvj4sxXZd0Sccjc31yHLMiZTndJRTksw2M6hQ/+XjRtHUlf3PAaTgZlXXMFNTz3FxAsuoK2+nta6Onx6PaVz57JmxQr2TZtGSKPp+07eew/sdoYNyyM+PgmPx4XLNQ5JOvqxecJgcTi2KLbviDhC79+/jQMHSsnK2hsVz3EKhZy0tX1IY+Nr6HTJPcMp+Wedhc/loqqsDL/Hg7b7sT6VhYUYPJ6+Pdbnh1HQKVMIhQ5fFbRVjIIOEUnykJWlzMPiI+IIXVGxE6PRHCEXxPrO661gz57FbNlSRGvrhySkpnLhLbew5LHHyB4/nqbKSjqbm3EmJPD9lVfy8c9/Tu3YsSffsHhAvKK83kMEAsqsbhP2hZYkiaqqvcTGxqDTdSgdZ1C4XCWUlV3K9u2z6ej4huSsLC5bsYLrHnqI5KwsmiorcbS10ZGSwtfXXXfy4ZQvv4SaGqzWVDIyRuJ0duDxDCcYPM2r6EKfuVxliuw37Avd0dFMIODDaAyTZX8GUWfnN+zYMZvS0gW4XKUMGzmSa+6/n6vvvRdzfDxNlZW47XZasrNZd+ONrF+8mLb09GM3JMuHPSB+Li6XHVCJB8QPIZerf+vcDZQIKHQLADrdIMxAh6m2to/YsqWI3bsX4fUeJKeggEWPPMJlK1ag1mhoqqzE63LRMGoUn/zbv/HNNddgT0o6ciNiVVBFiUL3or29CVmW0OvPnEJ3kWlqep1Nm8axf/+/Ewg0MmrqVJb9138xf/nyruGUqir8Ph/VBQV8eOutbLz0UhwmU9eX794NW7diMlnIz59CR0cLfn8qPt9xjujCgHO7lbnfIOwLbbNVodPFnLGDEbIcoL7+BTZuHEVFxf3IOCmYPZsbn3yScxctwt3ZSXNVFYFgkIqpU3lt8WJ2L1wIVmvPUXry5Dn4/V2jpeLi2NDw+ZR5izXsC93UVENMjAmtNjoviPWVJLmpqXmCjRtHUFX1OBqdRNH8+dz81FPMuOIKOpuaaKmpISY5mc+TknCXlMDIkQDk5uZjMBjFKOgQ8vsHbvHK/gj7Qre1NaHXR+8V7v4KBjs4dOiX3cMpf0Bv1DNz4UJueuopJs+bR4fNhr2lhcrKSrjjDgC0Wh2TJs2hvb2JUMiC2z1S2W/iDBAKOQgGB+hOun4I60L7/T48HgdarRaNZuj/44Qzv99GefntbNqUj832Kub4OOZcfz03PvEEMy6/HPdRDwMoKJiBJHXNwIvT7qHh99cP+T7DutBOZwdqtQa1OiAWju+F13uQvXuXsmVLES0tHxCfksL85cspXrDgiM87chQ0X4yCDgElTrvDutAeT9eN+Wq1T+Ek4c/lKmXnzsvYtu0cOjr+ecyfq9VqiosvwG5vRZZ1OJ3jFEh5ZvH5xBH6CIFA1wJ3otB9Z7d/y44dcygtvQSHY8cRfzZ27FRkWRajoENEHKGPEgj4kGVZFPoUtLV9zNatU9i9+3rc7gMAWK2pZGaOxOHoGgUNBOIUThndJGnoH5EkCh3VZJqa3mDz5nHs2/czfL56iovn4nZ3jYI6nWIUdDDJ8tCvXBLWhfb7u4osCn16ZDlIQ8Of2LhxFDrdW+h0AUIhsSroYJPloV9ZJ6wL7fO5AcRi8QNEkjzYbP+PadPewWT6lEAgAa9XjIIOFiUKrR3yPfaDx+NCrdbA4D1z4oykVnvIzt5MMLibYDBB6ThRSxT6KD6fB41GgySFdcyIpdW6xJrdg0qcch9Bo9F1v80iCi1EHnFR7Ch6vQFJkpBlndJRBOEU9GNxxwES1oXW6X4otDhCC5FHp7MO+T7DutAGQ0z3Inei0ELk0WpFoY+g0XQVWZLEKbcQeXS6pJN/0gAL+0J3rYdlUDqKIPSbKPRRugoNoZAFWQ7rqIJwDFHooxgMRrqGStQEg+JGAiGyaLWi0EeIjU1A1T0kFgzGKxtGEPpJHKGPYjbHI3U/fTEQEIUWIodOl4JGYxzy/YZ1oY1GM1qtllAoJI7QQkQxGscost+wLrRKpSI+Phm/3ytuIhAiiskkCn1cVmsafr+XQEAUWogcJlMfnhI6CCKg0MMIBHz4/SlKRxGEPjObJyiy37AvdHJyOoGAn1AolmDQonQcQegTs1mZ5Z3CvtBJScNQq7ti+nzDFE4jCCen0SQQE5OjyL4jotAAsiyLQgsRITa2SLF9h32hTSYLZnMcgYAfrzdL6TiCcFIJCecptu+wL7RKpSIrazRutx2vNwtZPBFHCHMJCecrtu+wLzTAiBEF+HweJMlIIJCsdBxB6JVKFUNc3AzF9h8RhU5Pz+35vceTp1wQQTiJ+PizUauVexBgRBQ6JSUTAEmScLmUmcARhL5Q8vUzREihdTo92dljcDo7cLuHixVMhLCVmKjc62eIkEIDFBaehctlB7S43SOVjm7XZtoAABE5SURBVCMIx1CrzcTGTlc2g6J774fc3HGoVCpkWRan3UJYSk5eiFqt7NljxCynmZiYQmJiKl6vC612DLKsQqUS72EJ4SMtbZHSESLnCK1SqSgsnIXD0U4oZMbny1Q6kiD0UKvjSUy8UOkYkVNogJEjC3tWMHE6xymcRhB+lJr6r4qfbkOEFTo9PReDwUgg4MfhmCRWAhXCRlraYqUjABFWaI1GQ2HhLDo6mgmFzDid+UpHEgQ0mlQSEuYoHQOIsEIDTJo0m2AwgCzL2O1TlY4jCKSnL0KlCo8qhUeKfkhPzyEtLRuXqxOPZzh+f6LSkYQzmCyryMq6XekYPSKu0CqVipkzL8bh6ABU2O1TlI4knMFiYy/EaAyfQaeIKzTAmDFFaLU6gsEADkeRuDgmKGbUqAeUjnCEiBksOVxMjImiojns2PE1ycmZOJ0FxMaWKR1L6Ob1qvjb39JxuzUEgyouvbQVn0/N2rWJaLUyCQlBbrzRhvawv32NjTpefTWt59+XLGkkLS3Aiy+m09Sk46KL2pkyxQnAq6+mcs45neTl+Yb6WzuCTjeBhITZimY4WsQe2iZNmtNzcaytbTayrFI6ktDt++/jSU8PcNddtfzsZ/W8+WYqb76Zyp131nH33bUYDBLbt8ce8TVffZXAZZe1ctddtcyaZWft2kTq6/Xo9TJ3313L+vVdyzjX1+sJBlWKlxlgxIhfKh3hGBFb6PT0HDIyRmK3txEIpOB0KrNsqnAsiyWE09n1V8vt1mCxhDCZQrjdXR/zeLo+drhrr21mzBgPAG1tWhITg7jdauLiguh0P474rlmTxMKFrUP0nfROpUojPf1apWMcIyJPuaHr4tjcudfwyiu/JS7OSlvbHCyWnWK+OwxMm+bgu+/i+NWv8nC7Ndx+ex2BgIrHHsvFZAqRne1j3Dj3MV9XU2Pgb39LR6+X+MUvanE6NTQ363C71RgMEnv2mBg2zM/WrRbq6w0UFzsYP/7Y7QyFnJy7UKk0iuz7RCL2CA2Qm5tPVtYo7PZWAoFkHA5l1kIWjrRhQyxWa4DHHqvkF7+o4bXXuk65f/nLah57rBK1GkpKzMd8XXa2j4ceqmLmTDtvvZVCUlKQxMQgzz2XwYUXtvP554lMn26nttbAkiWNfPqpUm9ZJpObu0KhfZ9YRBdapVJx/vnX4HY7kGWZ9vY54rV0GKioMFJQ0HXkzM72Y7PpkWVISQmgUkF+vpuqqpgjvqaszEyo+yx8yhQnFRVdT268+uoW7r67luZmHVOmOHA6NSQmBlGrQZKU+X+dk/OfqNUGRfZ9MhFdaOg6Smdnj6Gzs5VAIEkcpcNAamqAQ4e6Ctva+uPrYYej6xS1sjKG1FT/EV/zz3/GU1bWddQ+dCiGtLRAz5/5/Sq2b7dw1ll24uJCtLVpkWUUWQFWlrMZPvzWod9xH6lkOfIXxq2s3MsrrzxOenoeOl0nOTl/QK0OKh3rjOX1qli5Mh27XYMkqbj88ha8XjWffGJFq5VJTg6wdGkjTqeGNWuSWLKkieZmHStXpvUUddmyxp5Sf/ihlVGjPIwd23XR7G9/S6OxUc+sWXbmzOkc0u9tzJh/kJFx1ZDusz+iotCyLLNy5eM0NdV1L4TwFUlJXyodS4gyavU05szZpHSME4r4U27oei09b951eDxOJClER8fZBAJixlsYOJKkZuLEF5WOcVJRUWiAzMwRFBfPpaWlHlnW0tz8L0pHEqKIxbKMhITwvz4TNYUGmDPnCrRaHT6fB7d7NA6HGDYRTp/fn8aUKc8rHaNPoqrQFks88+cvoq3NhizLtLRcTChkVDqWEMEkSc3YsS+h1cac/JPDQFQVGmDSpHPIyRlLe3sToZCZlpaLlI4kRDCd7npycy9WOkafRV2h1Wo1l1xyI4GAr2ftMbt9otKxhAjk9Q5j5sy/KB2jX6Ku0AApKRmcf/7VNDXVIssyzc0L8PuTlI4lRBBJ0lBQsAqdLjJOtX8QlYUGmDHjYkaNmkBraz2yrMdm+1ckKWLvRRGGmF6/nOxsZZ9TdSqittAajYbLLvspOp0Bl8uO358mXk8LfeJyTeKss55TOsYpidpCA8TFJXL11bfR2dlCMBjEbi/G4ShQOpYQxlyuZGbMeBeNJvxujeyLqC40QF7eOM4990qamqqRZZmmpsvE62nhuPz+GEaOXEVycp7SUU5Z1Bca4OyzLyMvbxytrQ3IsoH6+sUEgxalYwlhRJLUmEwPk5+v/POpTscZUWitVssVV/wMvd6A3d5OMJhIQ8MiJEmvdDQhTHg8P2HWrHuUjnHazohCA8TFWbn++rvx+dy43U58vmHYbP8qlgAWaG+fwbx5f0Wtjvy/C5H/HfTDsGG5XHvtnXR2tuDzeXG7R9HUdLnSsQQFNTWN57zz1mAwRNb7zb05owoNMHLkBBYu/CktLXXdC/VPorV1rtKxBAU0NY1i5szVJCamKB1lwJxxhQaYOPFs5s37CY2N1UhSiPb22bS3n610LGEINTcPp7j4HXJyxigdZUCdkYUGmDVrAdOnX4jNVoUkSbS2zqO1NfImg4T+a2nJYfLktxkxIvzvb+6vM7bQKpWK+fOvZ9Kk2dhslUiSRHv7HJqbL1Zk8TlhaLS2ZlJQ8CajR0fnQw6jYk2x0xEKhVi7dhWbN39GWloeGo2G2NjtpKauEYv2R5nGxhGMH/8yhYXnKB1l0JzxhQaQJIn169/mm2/WkJaWg1arw2zeRXr6O6hUktLxhAFQXV3AxInPM3lyeD1cbqCJQneTZZlvvlnD+vVvk5KShU6nx2Q6QFra22g0yj8YTTg1sqzi4MHpzJr1LOPGFSsdZ9CJQh9GlmW2bFnHxx+/THJyJnp9DDpdC8OGvY5e36Z0PKGfQiE95eUX8C//8jtyc8cqHWdIiEIfR2npt7z//l+wWBKwWOJRq72kpb2N2VyhdDShj7zeOCorF3LVVf9FamqW0nGGzBl7lftEJk48mxtu+CXBYIC2NhuSFENDw2La2uaIK+ARoKlpBDbbHSxe/PQJy/zMM8+wceNG7r//fmpra/u1j6VLlx7x62DbvHkzra0nf4yuKHQvsrNHc8stD5OYmNb9XrVMW9v5NDQsFiuJhilJ0rF371nodPdw/fW/IjY2QelIA+Yf//hHnwotTrlPwufz8tlnr7N163qSkzMwGGLQaOykpn6A2VyudDyhm8uVzL5953LeebdTVHQuKtXJn0zZ1taG0WjE4XCQmJjIzTffzKxZs9iwYQPt7e288MILZGRk8OSTT7Jt2zZCoRCLFy/miiuuoL6+noyMjJ5ffxAIBLj//vupq6vDYDDw5JNPYrVaeeihh6ipqcHv97NixQrOOecc5s6dy5o1azCbzTzxxBOMHj0agK1bt9LW1sahQ4dYvnw5GRkZ3HnnneTm5vLMM88csb+jiUW2TsJgiGHBghvJzh7NBx/8FYPBSEJCCg0Ni4iN3UFy8qdoNF6lY57R6urG43BcxpIlt/fr9bLVagXAaPzxjMtisfDyyy/z1FNPsXbtWgoKCigvL+eNN97A7XZz+eWXM2/evJ5SHV2ud999l+TkZP7nf/6HDz/8kHXr1mEymdDr9bz66qs0NjaybNkyPv30015z7d+/nzfeeIPKykr+4z/+g/fee49x48bx4IMPnrDMIE65+0SlUjFp0jnccssjxMcn0dBwqHuJ4MlUV9+K03lmXEENN16vlZKSeVit93HTTQ8PyMWv4uKut7bS09NxOp3s3LmTadOmAWAymRg1ahRVVVW9fv2uXbuYMqVrCm3BggUsWrSInTt3MmPGDADS0tLQ6/V0dHT0uo3Jkyej0WhIT0/H4XD0K784QvdDWlo2N930IBs3fsqXX76DXh9DQkIKNtt1WCxlpKR8jEbjUTpm1JMkHbW1k2lsnMTll/87Y8YU9ekUuy8OX0tMluVjthsIBE5437RGo0GSjh1GOvyVrd/vP2YbgcCPz8PWak+9luII3U9arY6zz76Un/70UazWNGy2SgIBP05nIdXVt9HZOVUsmjCIOjpGsXnzZRiNS/npTx9n7NgpA1bm45kwYQIbN24EwOVyUV1dTW5ubq+fX1hYyIYNGwBYv349L7zwAoWFhT3baGhoQK1WExcXh8Viobm5mVAoRElJyQlzqFQqQqHQSfOKI/QpSk3N4oYbHmDLls9Zt+7vaLU6rNY0mpsvpaNjJklJ67BY9iodM2r4fImUl09FlidxzTU3MmJEwaAW+QfFxcVMmDCBxYsXEwwGueuuuzCZTL1+/iWXXMJ3333HkiVL0Gq1PPHEEyQlJbFp0yaWLl1KIBDg0UcfBWDJkiX8/Oc/Z/jw4YwaNeqEOaZPn86KFSt47rnnei6eHY+4yj0Ampvr+fzzNzhwoASTKY64OCsqlYqYmBqSkj7DaKxROmLE8vsTqa0tpLFxBLNnX8306fPR6w1KxwpbotADRJZlqqr28fnnr9PQUElcXBJmcxwAZvNerNYvMBiaFc0YSfx+K3V1RdTUDGPs2KnMn78IqzVV6VhhTxR6gEmSxL592/jss9ex21tJTEzDYDACMiZTBfHxGzCZKhiCs8WI5PMlU1c3mbq6dLKyxnDeeVeTlzduSE6vo4Eo9CAJBPyUlHzLl1++jcfjIj4+CZMpFgCdroWEhA3ExpaiVgdOsqXoJ8vg8eRRXz+GhoY0cnLyOffcq8jNHSuK3E+i0IPM5/OwZ88WvvlmDe3tTcTEmEhISEGlUqFWe4iL20Z8/BZ0ut7fl4xWwaAJu30iNTW52O0Ghg8v4NxzryQra5Qo8ikShR4ikiRRVbWX77//iIMHd6LRaElMTEOr1QEyMTHVxMaWYbHsjur3smVZg8s1htbWfOrqYpEkNfn5U5k582IyM0eKIp8mUWgFNDfXs23bl2zbtp5QKIjRaCE2NrF72EDCaDyExbIHs3kfWq1T6binLRg043aPwuEYQX19LD6fGoslnhkzLqKgYCZxcYlKR4waotAK8nhcHDq0i+3bv+LQod0AmEyxWCwJ3eWWiYmpxWisJCamGqOxGrXar2zoPpBl8PkycLtH09mZR1OTgUDAj0ajpaBgBpMmzSYra1TEPuExnIlChwmHo4OKijLKyr6junofsiyj0xmwWBIOe6qDhMFgIyamCqOxq+AajVvR3ADBoAWfLwOfbxhebzrt7Ql0dHT94NHrDYwZM4X8/Knk5uYTE9P7UIZw+kShw5DL5aCqai8HDpRQUVGGy2UHusZOuwpu7HmtqdHY0etb0elaun9tRa9vQavtHNBVS2VZRTAYSzAYRzAYRyCQgtebgceTitOpxu12EAoFga4puoKCGQwfXkBaWo44Eg8hUegwJ8synZ2t2GxVVFbu4cCBUjo6mlGr1UiShFarIybGhMFgQqvVHXZRKYhO14la7UGj8aJWe4/6vReVSkKWVYAKUCPLWiRJ1/2r4YgCB4NmQiEJr9eNx+MkFAp2X6lXk5aWQ07OWDIzR5KZOVK8JlaQKHQEcjg6aG1toKOjhaamGmy2apqba/F4XD138XTd8SOjVmvRaLRotV2/ajRa1GoNsiz33AEkyxKSJCFJISRJIhDwEQoFUKnUqFQqZBlkOURMjBmrNZ3c3LFkZIwgOTmDxMTU07o7SBhYotBRxOt109nZSmdnKz6fB5/Pg9ttx+Vy4HbbcbudeDxOAoGu2/dUKhUqlRqNRodeb0CnM2AwxBAfn0RiYioWSwJmc1zPPzqdeJ52uBOFFoQoIm7cFYQoIgotCFFEFFoQoogotCBEEVFoQYgiotCCEEVEoQUhiohCC0IUEYUWhCgiCi0IUUQUWhCiiCi0IEQRUWhBiCKi0IIQRUShBSGKiEILQhQRhRaEKCIKLQhRRBRaEKLI/we0f9G2nTsbsQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"dJchNcz8qxmu"},"source":["It can be seen that the minority class, with 11.7%, is a **moderately imbalanced sample**. And we will discuss this later in **Imbalanced Data Processing**. The following study focuses on the processing of the imbalanced dataset.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"DVbtsal7xtr1"},"source":["# 2.Feature Engineering"]},{"cell_type":"markdown","metadata":{"id":"5d_dRfb_xxyN"},"source":["The need to numerically manipulate non-numeric types of features was mentioned earlier, and the following ideas are used here for encoding.\n","- Label Encoding\n","- One Hot Encoding\n","- Dummy Variable Trap\n","\n","Here we only discuss the case where the features are categorical variables and do not consider the case where the feature values are text. On the one hand textual variables do not appear in this dataset, and on the other hand this is a deeper area of research for NLP which we will not discuss in the project."]},{"cell_type":"markdown","metadata":{"id":"MC1DvYSlCfXq"},"source":["## 2.1 Label Encoding"]},{"cell_type":"markdown","metadata":{"id":"ffNnJRUpCkKI"},"source":["Label encoding: Encodes the category of a categorical variable as a number.\n","\n","Implemented with sklearn.preprocessing.LabelEncoder to encode categorical variables containing k categories as 0, 1, 2, ... (k-1)\n","\n","Label encoding is generally not used for features, but for target variables.\n","\n","Suppose a representative feature, `'gender'`, contains two categories: `'male'`, `'female'`. The tag encoding encodes the categories as integers, with 0 representing male and 1 representing female. However, this does not fit the assumptions behind the model as machine learning models assume that the data has an arithmetic meaning, for example 0 < 1, which implies male < female, but this relationship does not hold. Generally a unique hot code would be used to deal with categorical features and the label code would only be used for the categorical target.\n","\n","So we will apply label encoding only on the target variable column `'y'`:"]},{"cell_type":"code","metadata":{"id":"nHHin2BKDY7t","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632118330625,"user_tz":-480,"elapsed":407,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"216893d3-f9cd-4192-a878-d7bc18715cbe"},"source":["from sklearn.preprocessing import LabelEncoder\n","\n","# target varible\n","y = np.array(raw_df['y']).tolist()\n","# create LabelEncoder object\n","le = LabelEncoder()\n","# fit the data\n","le.fit(y)\n","# See what categories are included\n","print(\"classes: \", le.classes_)\n","# encode them into numbers\n","y_labeled = le.transform(y)\n","print(\"encoded labels: \", y_labeled)\n","# Call inverse_transform for inverse operations\n","print(\"inverse encoding: \", le.inverse_transform([0, 1]))\n","# write into the raw_df\n","Y = pd.DataFrame(y_labeled,columns=['y'])"],"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["classes:  ['no' 'yes']\n","encoded labels:  [0 0 0 ... 1 0 0]\n","inverse encoding:  ['no' 'yes']\n"]}]},{"cell_type":"markdown","metadata":{"id":"JRBSjpeoHDEy"},"source":["## 2.2 One Hot Encoding"]},{"cell_type":"markdown","metadata":{"id":"kGZxfeLbHIKe"},"source":["One hot encoding: transforms a categorical variable containing m categories into a binary matrix of $n \\times m$, where $n$ is the number of observations and $m$ is the number of categories.\n","\n","Suppose the categorical variable `'car_type'`, denoting the type of car, contains the categories `(BMW, Tesla, Audi)`. The one hot encoding will make each category a new variable/feature, with `1` indicating that the observation contains the category and `0` indicating that it does not.\n","\n","<div align=center><img width=35% src=\"https://cdn.jsdelivr.net/gh/shimmerjordan/DSML_project_bank_classifying@main/pic/one-hot.png\"></div>\n","\n","Calling `pd.get_dummies` to implement the one hot encoding is more convenient than sklearn's interface because it allows us to manipulate the data frame directly.\n","\n","By default, `pd.get_dummies` treats variables with data type `'object`' as categorical variables, or you can provide the name of the variable to be coded.\n","\n","On the other hand, the one hot coding introduces $K$ new features, representing each of the $K$ categories of the categorical variable. **However, this cannot be done if we are using a regression model, as this would lead to multicollinearity.**"]},{"cell_type":"markdown","metadata":{"id":"BcocfihfIjGB"},"source":["## 2.3 Dummy Variable Trap"]},{"cell_type":"markdown","metadata":{"id":"4yx-EQ_lIvyI"},"source":["Here we first extract the list of variable names that do not belong to the numeric type categ_columns. Prefix the dummy variable with the name of the dummy variable (containing only \"yes\" or \"no\"). Also we noticed that there are many variables with unknown values in the data, which we choose to discard."]},{"cell_type":"code","metadata":{"id":"vHnUk6O4Nr7O","colab":{"base_uri":"https://localhost:8080/","height":241},"executionInfo":{"status":"ok","timestamp":1632118334132,"user_tz":-480,"elapsed":384,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"d7eb2298-6c78-4914-8a0d-811dcffe1c00"},"source":["categ_columns = ['job','marital','education','default','housing','loan','contact','month','poutcome']\n","new_df = raw_df.drop(columns=categ_columns)\n","for categ_col in categ_columns:\n","  if categ_col in ['default','housing','loan']:\n","    c_df = pd.get_dummies(raw_df[categ_col],prefix=categ_col)\n","  else:\n","    c_df = pd.get_dummies(raw_df[categ_col])\n","  if 'unknown' in c_df.columns.tolist():\n","    c_df = c_df.drop(columns=['unknown'])\n","  new_df = pd.concat((new_df,c_df),axis=1)\n","new_df = new_df.drop(columns=['other'],axis=1)\n","new_df['y'] = Y\n","new_df.head()"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>balance</th>\n","      <th>day</th>\n","      <th>duration</th>\n","      <th>campaign</th>\n","      <th>pdays</th>\n","      <th>previous</th>\n","      <th>y</th>\n","      <th>admin.</th>\n","      <th>blue-collar</th>\n","      <th>entrepreneur</th>\n","      <th>housemaid</th>\n","      <th>management</th>\n","      <th>retired</th>\n","      <th>self-employed</th>\n","      <th>services</th>\n","      <th>student</th>\n","      <th>technician</th>\n","      <th>unemployed</th>\n","      <th>divorced</th>\n","      <th>married</th>\n","      <th>single</th>\n","      <th>primary</th>\n","      <th>secondary</th>\n","      <th>tertiary</th>\n","      <th>default_no</th>\n","      <th>default_yes</th>\n","      <th>housing_no</th>\n","      <th>housing_yes</th>\n","      <th>loan_no</th>\n","      <th>loan_yes</th>\n","      <th>cellular</th>\n","      <th>telephone</th>\n","      <th>apr</th>\n","      <th>aug</th>\n","      <th>dec</th>\n","      <th>feb</th>\n","      <th>jan</th>\n","      <th>jul</th>\n","      <th>jun</th>\n","      <th>mar</th>\n","      <th>may</th>\n","      <th>nov</th>\n","      <th>oct</th>\n","      <th>sep</th>\n","      <th>failure</th>\n","      <th>success</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>58</td>\n","      <td>2143</td>\n","      <td>5</td>\n","      <td>261</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>44</td>\n","      <td>29</td>\n","      <td>5</td>\n","      <td>151</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>33</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>76</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>47</td>\n","      <td>1506</td>\n","      <td>5</td>\n","      <td>92</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>33</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>198</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   age  balance  day  duration  campaign  ...  nov  oct  sep  failure  success\n","0   58     2143    5       261         1  ...    0    0    0        0        0\n","1   44       29    5       151         1  ...    0    0    0        0        0\n","2   33        2    5        76         1  ...    0    0    0        0        0\n","3   47     1506    5        92         1  ...    0    0    0        0        0\n","4   33        1    5       198         1  ...    0    0    0        0        0\n","\n","[5 rows x 47 columns]"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"Ph9dw6PlXnzX"},"source":["## 2.4 Feature Crossover"]},{"cell_type":"markdown","metadata":{"id":"S2e2tgMaSJf8"},"source":["Feature crossover is a way of processing data features, increasing the dimensionality of features by feature combination to seek a better training effect.\n","In practical scenarios, we often encounter situations where linear classification is not possible in the following samples (it is impossible to draw a straight line to separate the following yellow and blue dots), **so feature combination is a way to let linear models learn non-linear features**.\n","\n","\n","<div align=center><img width=35% src=\"https://cdn.jsdelivr.net/gh/shimmerjordan/DSML_project_bank_classifying@main/pic/feature-cross.png\"></div>\n","\n","For example, a large number of feature combinations are used in advertising models, because LR is the most commonly used model in advertising promotion, but LR itself is not complex enough, in order to make LR learn more complex nonlinear features, often using the original features plus feature combinations."]},{"cell_type":"code","metadata":{"id":"STJcghPwg60y","colab":{"base_uri":"https://localhost:8080/","height":241},"executionInfo":{"status":"ok","timestamp":1632118485678,"user_tz":-480,"elapsed":148872,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"8413aeed-116d-4492-c00a-86dd07cab363"},"source":["mg_df = pd.DataFrame()\n","for i in range(len(new_df)):\n","  mg_df.loc[i,'housing_y_duration'] = new_df.loc[i,'duration']*new_df.loc[i,'housing_yes']\n","  mg_df.loc[i,'housing_y_age'] = new_df.loc[i,'age']*new_df.loc[i,'housing_yes']\n","  mg_df.loc[i,'success_duration'] = new_df.loc[i,'duration']*new_df.loc[i,'success']\n","  mg_df.loc[i,'success_age'] = new_df.loc[i,'age']*new_df.loc[i,'success']\n","  mg_df.loc[i,'sucess_hy'] = new_df.loc[i,'housing_yes']*new_df.loc[i,'success']\n","  mg_df.loc[i,'duration_previous'] = new_df.loc[i,'previous']*new_df.loc[i,'duration']\n","  mg_df.loc[i,'telephone_duration'] = new_df.loc[i,'telephone']*new_df.loc[i,'duration']\n","  mg_df.loc[i,'duration_balance'] = new_df.loc[i,'balance']*new_df.loc[i,'duration']\n","  mg_df.loc[i,'duration_age'] = new_df.loc[i,'age']*new_df.loc[i,'duration']\n","  mg_df.loc[i,'duration_cellular'] = new_df.loc[i,'cellular']*new_df.loc[i,'duration']\n","mg_df = pd.concat((new_df,mg_df),axis=1)\n","mg_df.head()"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>balance</th>\n","      <th>day</th>\n","      <th>duration</th>\n","      <th>campaign</th>\n","      <th>pdays</th>\n","      <th>previous</th>\n","      <th>y</th>\n","      <th>admin.</th>\n","      <th>blue-collar</th>\n","      <th>entrepreneur</th>\n","      <th>housemaid</th>\n","      <th>management</th>\n","      <th>retired</th>\n","      <th>self-employed</th>\n","      <th>services</th>\n","      <th>student</th>\n","      <th>technician</th>\n","      <th>unemployed</th>\n","      <th>divorced</th>\n","      <th>married</th>\n","      <th>single</th>\n","      <th>primary</th>\n","      <th>secondary</th>\n","      <th>tertiary</th>\n","      <th>default_no</th>\n","      <th>default_yes</th>\n","      <th>housing_no</th>\n","      <th>housing_yes</th>\n","      <th>loan_no</th>\n","      <th>loan_yes</th>\n","      <th>cellular</th>\n","      <th>telephone</th>\n","      <th>apr</th>\n","      <th>aug</th>\n","      <th>dec</th>\n","      <th>feb</th>\n","      <th>jan</th>\n","      <th>jul</th>\n","      <th>jun</th>\n","      <th>mar</th>\n","      <th>may</th>\n","      <th>nov</th>\n","      <th>oct</th>\n","      <th>sep</th>\n","      <th>failure</th>\n","      <th>success</th>\n","      <th>housing_y_duration</th>\n","      <th>housing_y_age</th>\n","      <th>success_duration</th>\n","      <th>success_age</th>\n","      <th>sucess_hy</th>\n","      <th>duration_previous</th>\n","      <th>telephone_duration</th>\n","      <th>duration_balance</th>\n","      <th>duration_age</th>\n","      <th>duration_cellular</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>58</td>\n","      <td>2143</td>\n","      <td>5</td>\n","      <td>261</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","  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<td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>76.0</td>\n","      <td>33.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>152.0</td>\n","      <td>2508.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>47</td>\n","      <td>1506</td>\n","      <td>5</td>\n","      <td>92</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>92.0</td>\n","      <td>47.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>138552.0</td>\n","      <td>4324.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>33</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>198</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>198.0</td>\n","      <td>6534.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   age  balance  day  ...  duration_balance  duration_age  duration_cellular\n","0   58     2143    5  ...          559323.0       15138.0                0.0\n","1   44       29    5  ...            4379.0        6644.0                0.0\n","2   33        2    5  ...             152.0        2508.0                0.0\n","3   47     1506    5  ...          138552.0        4324.0                0.0\n","4   33        1    5  ...             198.0        6534.0                0.0\n","\n","[5 rows x 57 columns]"]},"metadata":{},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"TtERc0ak5UmQ"},"source":["The following is to deal with the `date`, `mg_df` is the DataFrame containing the synthetic feature, `new_df` is onehot after the DataFrame does not contain the synthetic feature."]},{"cell_type":"code","metadata":{"id":"TKiVedycvsKh","executionInfo":{"status":"ok","timestamp":1632118504471,"user_tz":-480,"elapsed":1188,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["date = []\n","for i in range(len(raw_df)):\n","  date.append(raw_df.loc[i,'month']+'-'+str(raw_df.loc[i,'day']))\n","mg_df['date'] = date\n","mg_df['date'] = LabelEncoder().fit_transform(mg_df['date'])\n","new_df['date'] = date\n","new_df['date'] = LabelEncoder().fit_transform(new_df['date'])"],"execution_count":8,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ZhYtHs619PZD"},"source":["# 3.Data Split"]},{"cell_type":"markdown","metadata":{"id":"2ZBWQ7x19ldN"},"source":["To avoid over-optimistic performance, we only do feature selection and imbalanced data processing on the `train_set`.\n","We use 10% of the whole dataset as test_set, 90% as `train_set`, and we will perform 5-fold cross validation on the `train_set` to tune the parameters."]},{"cell_type":"code","metadata":{"id":"UZKzsOXT_wMH","executionInfo":{"status":"ok","timestamp":1632118507089,"user_tz":-480,"elapsed":662,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["from sklearn.model_selection import train_test_split\n","train_set, test_set = train_test_split(new_df, test_size=0.1)\n","train_g_set, test_g_set = train_test_split(mg_df, test_size=0.1)"],"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"84bt9iN0LK9z"},"source":["# 4.Feature Selection"]},{"cell_type":"markdown","metadata":{"id":"sgCqxi5Xz8Vr"},"source":["By feature selection, we can make the model easier to train and better to perform."]},{"cell_type":"code","metadata":{"id":"ptS4Nre_4ao5","executionInfo":{"status":"ok","timestamp":1632118509528,"user_tz":-480,"elapsed":452,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["from sklearn.feature_selection import SelectKBest\n","from sklearn.feature_selection import f_classif\n","from sklearn.preprocessing import MinMaxScaler\n","\n","def select_BestKF(X_train,y_train,K):\n","  col = X_train.columns.tolist()\n","  X_train = MinMaxScaler().fit_transform(X_train)\n","  select_ = SelectKBest(f_classif, k=K).fit(X_train,y_train)\n","  score = pd.DataFrame(select_.scores_,index=col,columns=['score'])\n","  score = score.sort_values(by='score',ascending=False)\n","  select_col = score.index.tolist()[:K]\n","\n","  return select_col, score[:K]\n","\n","bestKFs, score = select_BestKF(train_set.drop(['y'], axis=1), train_set['y'], 40)\n","bestKFs_g, score_g = select_BestKF(train_g_set.drop(['y'], axis=1), train_g_set['y'], 40)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"_7ZPX1AYJ5F5","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1632118511484,"user_tz":-480,"elapsed":2,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"55cc7086-533d-4a2d-afe7-a6fd00562ba6"},"source":["plt.plot(range(len(score)),score)\n","plt.axhline(0, c='green', ls=\"--\", lw=1)\n","plt.title('Feature Score for train_set')\n","plt.xlabel('k')\n","plt.show()"],"execution_count":11,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"2bXff-UZNJ8X"},"source":["We first choose `k = 22`, the scores are very colse to $0$, so we may use top 22 features on the pre-processed original dataset `new_df`."]},{"cell_type":"code","metadata":{"id":"ugTJyKIuQEn2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632118513335,"user_tz":-480,"elapsed":312,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"9975afdf-68bb-4dfc-af1a-65711a9efab4"},"source":["print(\"selected features:\" ,bestKFs[:22])"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["selected features: ['duration', 'success', 'housing_yes', 'housing_no', 'cellular', 'mar', 'oct', 'sep', 'pdays', 'may', 'previous', 'retired', 'student', 'campaign', 'blue-collar', 'dec', 'single', 'tertiary', 'loan_yes', 'loan_no', 'apr', 'married']\n"]}]},{"cell_type":"code","metadata":{"id":"GavpuIqaJ5Rw","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1632118518030,"user_tz":-480,"elapsed":655,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"74e7b9d2-13d9-4b1d-a9af-62332dddb7cb"},"source":["plt.plot(range(len(score_g)),score_g)\n","plt.axhline(0, c='green', ls=\"--\", lw=1)\n","plt.title('Feature Score for train_g_set')\n","plt.xlabel('k')\n","plt.show()"],"execution_count":13,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"6nI6TUyQNKpk"},"source":["We choose the value of `k = 32` similarly for the dataset containing the crossed features `mg_df`."]},{"cell_type":"code","metadata":{"id":"ZUa8B2RbQ2aD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632118520114,"user_tz":-480,"elapsed":3,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"79c74769-daa3-4196-ab73-fb628ce06849"},"source":["print(\"selected features:\" ,bestKFs_g[:32])"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["selected features: ['duration', 'duration_age', 'duration_cellular', 'success', 'success_age', 'success_duration', 'duration_balance', 'housing_y_duration', 'duration_previous', 'housing_no', 'housing_yes', 'cellular', 'sucess_hy', 'housing_y_age', 'mar', 'oct', 'sep', 'may', 'pdays', 'previous', 'telephone_duration', 'retired', 'student', 'dec', 'campaign', 'blue-collar', 'loan_yes', 'loan_no', 'tertiary', 'apr', 'single', 'married']\n"]}]},{"cell_type":"code","metadata":{"id":"JcaM2NhkNF51","executionInfo":{"status":"ok","timestamp":1632118521689,"user_tz":-480,"elapsed":1,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["X_train = train_set[bestKFs[:22]]\n","y_train = train_set['y']\n","X_test = test_set[bestKFs[:22]]\n","y_test = test_set['y']\n","\n","X_g_train = train_g_set[bestKFs_g[:32]]\n","y_g_train = train_g_set['y']\n","X_g_test = test_g_set[bestKFs_g[:32]]\n","y_g_test = test_g_set['y']\n","\n","X_train_all_feature = train_set.drop(columns=['y'])\n","X_test_all_feature = test_set.drop(columns=['y'])"],"execution_count":15,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"os668_QK-89R"},"source":["`X_train_all_feature` and `X_test_all_feature` here are used in the `knn` method to show the impact of feature selection"]},{"cell_type":"markdown","metadata":{"id":"pB2_qTIgsKuw"},"source":["# 5.Imbalanced Data Processing"]},{"cell_type":"markdown","metadata":{"id":"YBFRryczCMPP"},"source":["As we mentioned before that the target feature `y` of this dataset are strongly imbalanced, and here we balance the imbalanced dataset after completing the numerical operation of the non-numerical features."]},{"cell_type":"markdown","metadata":{"id":"HhUwpGBBskJ3"},"source":["### 5.1 Random undersampling "]},{"cell_type":"markdown","metadata":{"id":"wfYzwT4-sv7G"},"source":["There is also a method available in the `imblearn` library for undersampling, `under_sampling.RandomUnderSampler`, which we can use to bring in the method and call it. As can be seen, the original sample of `'no'` has 39922, and after undersampling it becomes the same number as `'yes'`, achieving a 50%/50% category distribution."]},{"cell_type":"code","metadata":{"id":"zYptZoN5sgM8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632118524803,"user_tz":-480,"elapsed":896,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"82f03e53-f73c-4d22-afb1-f89a070f8226"},"source":["# import relative methods\n","from imblearn.under_sampling import RandomUnderSampler\n","from collections import Counter\n","import warnings\n","warnings.simplefilter(action='ignore', category=FutureWarning)\n","def RandomUnderSampler_imbc(X, y):\n","  # splite X and y\n","  X_train = X\n","  y_train = y\n","  # calculate the current category share\n","  print(\"Before undersampling: \", Counter(y_train))\n","  # Calling methods for undersampling\n","  undersample = RandomUnderSampler(sampling_strategy='majority')\n","  # Obtaining undersampled samples\n","  X_train_under, y_train_under = undersample.fit_resample(X_train, y_train)\n","  # calculate percentage of categories after undersampling\n","  print(\"After undersampling: \", Counter(y_train_under))\n","  return X_train_under, y_train_under\n","\n","#RandomUnderSampler_imbc(X_train, y_train)"],"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n","  \"(https://pypi.org/project/six/).\", FutureWarning)\n","/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n","  warnings.warn(message, FutureWarning)\n"]}]},{"cell_type":"markdown","metadata":{"id":"rUOjRNeOK_kO"},"source":["## 5.2 Oversampling with SMOTE"]},{"cell_type":"markdown","metadata":{"id":"9KELIzb2LeF-"},"source":["Among the oversampling techniques, SMOTE(Synthetic Minority Oversampling Technique) is considered as one of the most popular data sampling algorithms, which is based on a modified version of the random oversampling algorithm. Since random oversampling only adopts the strategy of simply copying samples for sample augmentation, this way leads to a more immediate problem of overfitting. Therefore, the basic idea of SMOTE is to analyze a few classes of samples and synthesize new samples to add to the dataset.\n","\n","\n","> The algorithm flow is as follows.\n",">\n","> (1) For each sample x in the minority class, calculate its distance to all samples in the minority class sample set in terms of Euclidean distance to obtain its k-nearest neighbors.\n",">\n","> (2) Set a sampling ratio according to the sample imbalance ratio to determine the sampling multiplicity N. For each sample x in the minority class, select a number of samples at random from its k-nearest neighbors, assuming the selected nearest neighbors are xn.\n",">\n","> (3) For each randomly selected nearest neighbor xn, construct a new sample with the original sample according to the following formula, respectively.\n"]},{"cell_type":"code","metadata":{"id":"ZV7_531ZK_vu","executionInfo":{"status":"ok","timestamp":1632118528223,"user_tz":-480,"elapsed":650,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["from imblearn.over_sampling import SMOTE\n","def SMOTE_imbc(X, y):\n","  X_train = X\n","  y_train = y\n","  # calculate the current category share\n","  print(\"Before undersampling: \", Counter(y_train))\n","  # Obtaining undersampled samples\n","  X_train_SMOTE, y_train_SMOTE = SMOTE().fit_resample(X_train, y_train)\n","  # calculate percentage of categories after oversampling\n","  print(\"After undersampling: \", Counter(y_train_SMOTE))\n","  return X_train_SMOTE, y_train_SMOTE\n","\n","#SMOTE_imbc(X_train, y_train)"],"execution_count":17,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nnrP9587svGW"},"source":["## 5.3 Combination of undersampling and oversampling (pipeline)"]},{"cell_type":"markdown","metadata":{"id":"0nFg1z3POE2W"},"source":["So if we need to use both oversampling and undersampling, how do we do it? It is very simple to use pipeline."]},{"cell_type":"code","metadata":{"id":"cCnG7O1hOEQ2","executionInfo":{"status":"ok","timestamp":1632118530176,"user_tz":-480,"elapsed":382,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["from imblearn.pipeline import Pipeline\n","from sklearn.model_selection import cross_val_score\n","def UandO_imbc_withModel(X_train, y_train, o=0.4, u=0.5):\n","  #  define the pipeline\n","  X = X_train\n","  y = y_train\n","  over = SMOTE(sampling_strategy=o)\n","  under = RandomUnderSampler(sampling_strategy=u)\n","  steps = [('over', over), ('under', under)]\n","  pipeline = Pipeline(steps=steps)\n","  return pipeline.fit_resample(X, y)\n","# UandO_imbc_withModel(X_train, y_train)"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0M0IDbIaSNos"},"source":["## 5.4 Get the best sampling rate"]},{"cell_type":"markdown","metadata":{"id":"4EEbEkZ4SRBs"},"source":["In the above examples, we have sampled 50:50 by default, but this sampling ratio is not optimal, so we introduce a concept called optimal sampling rate, and then we find this optimal point by setting the sampling ratio and sampling grid search method."]},{"cell_type":"code","metadata":{"id":"JC1pTkLTSVxj","executionInfo":{"status":"ok","timestamp":1632118532731,"user_tz":-480,"elapsed":397,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["from sklearn.svm import SVC\n","def BestSamplingRate_imbc(X_train, y_train):\n","  X = X_train\n","  y = y_train\n","  over_values = [0.3,0.4,0.5]\n","  under_values = [0.7,0.6,0.5]\n","  for o in over_values:\n","    for u in under_values:\n","      # define pipeline\n","      model = SVC()\n","      over = SMOTE(sampling_strategy=o)\n","      under = RandomUnderSampler(sampling_strategy=u)\n","      steps = [('over', over), ('under', under), ('model', model)]\n","      pipeline = Pipeline(steps=steps)\n","\n","      # fit with pipline\n","      out = pipeline.fit(X, y)\n","\n","      # evaluate pipeline\n","      score = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=5, n_jobs=-1)\n","      scores = np.mean(score)\n","      print('SMOTE oversampling rate:%.1f, Random undersampling rate:%.1f , Mean ROC AUC: %.3f' % (o, u, scores))\n","\n","      max_o, max_u, max_s = 0, 0, 0\n","      if scores > max_s: \n","        max_o, max_u, max_s = o, u, scores\n","  return max_o, max_u, max_s"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"id":"0KfIJDZb6IAm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1631974949848,"user_tz":-480,"elapsed":2213180,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"464e1c13-7de8-4b7c-d1c0-6d117506a780"},"source":["max_o, max_u, max_s = BestSamplingRate_imbc(X_train, y_train)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["SMOTE oversampling rate:0.3, Random undersampling rate:0.7 , Mean ROC AUC: 0.850\n","SMOTE oversampling rate:0.3, Random undersampling rate:0.6 , Mean ROC AUC: 0.852\n","SMOTE oversampling rate:0.3, Random undersampling rate:0.5 , Mean ROC AUC: 0.852\n","SMOTE oversampling rate:0.4, Random undersampling rate:0.7 , Mean ROC AUC: 0.852\n","SMOTE oversampling rate:0.4, Random undersampling rate:0.6 , Mean ROC AUC: 0.853\n","SMOTE oversampling rate:0.4, Random undersampling rate:0.5 , Mean ROC AUC: 0.853\n","SMOTE oversampling rate:0.5, Random undersampling rate:0.7 , Mean ROC AUC: 0.853\n","SMOTE oversampling rate:0.5, Random undersampling rate:0.6 , Mean ROC AUC: 0.854\n","SMOTE oversampling rate:0.5, Random undersampling rate:0.5 , Mean ROC AUC: 0.855\n"]}]},{"cell_type":"markdown","metadata":{"id":"HtmaDBUi9d1K"},"source":["We can see that `Mean ROC AUC` peaks at 0.855 when SMOTE oversampling rate and Random undersampling rate are both 0.5. Thus, we use the best rate `o=0.5` and `u=0.5` to process imbalanced data for the following procedures.\n","\n","Similarly, `X_train` and `y_train` are the date set without crossed features which `X_g_train` and `y_g_train` contain. `X_train_all_feature` and `X_test_all_feature` here are used in the `knn` method to show the impact of feature selection"]},{"cell_type":"code","metadata":{"id":"fhriqrQ89z8z","executionInfo":{"status":"ok","timestamp":1632118536155,"user_tz":-480,"elapsed":482,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["X_train, y_train = UandO_imbc_withModel(X_train, y_train, 0.5, 0.5)"],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"id":"W17NJmSO-kNl","executionInfo":{"status":"ok","timestamp":1632118537364,"user_tz":-480,"elapsed":2,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["X_g_train, y_g_train = UandO_imbc_withModel(X_g_train, y_g_train, 0.5, 0.5)"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"kzIPopOg_ZHq","executionInfo":{"status":"ok","timestamp":1632118539055,"user_tz":-480,"elapsed":498,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["X_train_all_feature, y_train_all_feature=UandO_imbc_withModel(X_train_all_feature, train_set['y'], 0.5, 0.5)"],"execution_count":22,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ErjlUEV-CNmD"},"source":["# 6.Experiment"]},{"cell_type":"code","metadata":{"id":"VorbxcUkTy8p","executionInfo":{"status":"ok","timestamp":1632118540093,"user_tz":-480,"elapsed":567,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["mccs=[]\n","mccs_g=[]\n","mccs_a=[]\n","models=['KNN','SVM',\"DT\",\"RF\",\"AdaBoost\"]"],"execution_count":23,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"B8B1-4VoJkSI"},"source":["## 6.1 K Nearest Neighbor"]},{"cell_type":"markdown","metadata":{"id":"azgqVsw7xPcA"},"source":["### Data standardization"]},{"cell_type":"markdown","metadata":{"id":"wFmyBq7cijzA"},"source":["For fitting with KNN model, we need to standardlize the data"]},{"cell_type":"code","metadata":{"id":"LzhIaatHisLy","executionInfo":{"status":"ok","timestamp":1632118543805,"user_tz":-480,"elapsed":501,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["from sklearn.preprocessing import StandardScaler\n","\n","standardScaler1 = StandardScaler() \n","standardScaler1.fit(X_train) \n","X_train_standard = standardScaler1.transform(X_train) \n","X_test_standard = standardScaler1.transform(X_test)\n","\n","standardScaler2 = StandardScaler() \n","standardScaler2.fit(X_train_all_feature) \n","X_train_all_feature_standard = standardScaler2.transform(X_train_all_feature) \n","X_test_all_feature_standard = standardScaler2.transform(X_test_all_feature)  \n","\n","standardScaler3 = StandardScaler() \n","standardScaler3.fit(X_g_train) \n","X_g_train_standard = standardScaler3.transform(X_g_train) \n","X_g_test_standard = standardScaler3.transform(X_g_test) "],"execution_count":24,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nyOkc0DGxuBc"},"source":["### Parameters tuning with CV"]},{"cell_type":"markdown","metadata":{"id":"AWJYJGJ-UpsN"},"source":["We use 5-fold cross validation to find a proper `k` for KNN model"]},{"cell_type":"code","metadata":{"id":"_PVAMtlcdvo8","executionInfo":{"status":"ok","timestamp":1632118546037,"user_tz":-480,"elapsed":1,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["from sklearn.model_selection import cross_val_score\n","from sklearn.neighbors import KNeighborsClassifier"],"execution_count":25,"outputs":[]},{"cell_type":"code","metadata":{"id":"ewHfaO--UkMK","colab":{"base_uri":"https://localhost:8080/","height":293},"executionInfo":{"status":"ok","timestamp":1631976116825,"user_tz":-480,"elapsed":1165763,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"5efedf01-6181-4809-c257-14cda0f300d9"},"source":["k_range = range(1,15)\n","scores = []\n","for k in k_range:\n","  knn = KNeighborsClassifier(n_neighbors=k)\n","  knn.fit(X_train_standard,y_train)\n","  train_accuracy = knn.score(X_train_standard, y_train)\n","  test_accuracy = knn.score(X_test_standard, y_test)\n","  cv_accuracy = np.mean(cross_val_score(knn, X_train_standard, y_train, cv=5, scoring='accuracy'))\n","  scores.append([k, train_accuracy, test_accuracy, cv_accuracy])\n","\n","scores = pd.DataFrame(data=scores,columns=['k', 'train accuracy', 'test accuracy', 'cv accuracy'])\n","results = pd.melt(scores,id_vars=['k'],var_name='type',value_name='accuracy')\n","\n","sns.lineplot(x='k', y='accuracy', hue='type', data=results)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f0370312990>"]},"metadata":{},"execution_count":37},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"9MokR9gCVpNH"},"source":["We can see that after `k=3`, the accuracy rise slowly, so we choose the value 3 as `k` to keep the balance between time and accuracy.\n","\n","We use all features in knn2 and only selected features in knn1. Later we can see the difference between knn1 and knn2.\n","\n","In addition, we use knn3 on the `X_g_train` to show the impact of feature generation."]},{"cell_type":"code","metadata":{"id":"gfC1bl8UJrK1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632118559997,"user_tz":-480,"elapsed":11574,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"934b67a4-cd63-4ac9-d4de-74a36caa671f"},"source":["knn1 = KNeighborsClassifier(n_neighbors=3)\n","knn1.fit(X_train_standard,y_train)\n","                                \n","knn2 = KNeighborsClassifier(n_neighbors=3)\n","knn2.fit(X_train_all_feature_standard,y_train_all_feature)\n","\n","knn3 = KNeighborsClassifier(n_neighbors=3)\n","knn3.fit(X_g_train_standard,y_g_train)"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n","                     metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n","                     weights='uniform')"]},"metadata":{},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"VNLC86ndx1rs"},"source":["### Evaluation"]},{"cell_type":"markdown","metadata":{"id":"zR-ryvFRWZXJ"},"source":["Define the evaluation function and evaluate"]},{"cell_type":"code","metadata":{"id":"hXg9XjhDg5gS","executionInfo":{"status":"ok","timestamp":1632118562046,"user_tz":-480,"elapsed":321,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["from sklearn.metrics import confusion_matrix\n","def evaluation(y_test,predict_y):\n","  conf_matrix = confusion_matrix(y_test.values, predict_y)\n","  tn, fp, fn, tp = conf_matrix.ravel()\n","  mcc = (tp*tn - fp*fn)/(((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn))** 0.5)\n","  print(\"TN,FP,FN,TP:\",tn, fp, fn, tp)\n","  print(\"mcc value:\",mcc)\n","  sns.heatmap(conf_matrix, annot=True, fmt='g', cmap=\"YlGnBu\")\n","  print(\"\")\n","  return mcc"],"execution_count":27,"outputs":[]},{"cell_type":"code","metadata":{"id":"My2ls5VsYFIO","colab":{"base_uri":"https://localhost:8080/","height":335},"executionInfo":{"status":"ok","timestamp":1632118567709,"user_tz":-480,"elapsed":4405,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"2fcb8556-2253-4cc4-8be4-517baad043c2"},"source":["y1_predict = knn1.predict(X_test_standard)\n","print(\"selected features:\")\n","mccs.append(evaluation(y_test,y1_predict))"],"execution_count":28,"outputs":[{"output_type":"stream","name":"stdout","text":["selected features:\n","TN,FP,FN,TP: 3622 364 241 295\n","mcc value: 0.420541925118684\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"gbtUEmNgqmH3","colab":{"base_uri":"https://localhost:8080/","height":335},"executionInfo":{"status":"ok","timestamp":1632118596692,"user_tz":-480,"elapsed":25779,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"a101fe75-f26e-4593-d704-968c9b74b904"},"source":["y2_predict = knn2.predict(X_test_all_feature_standard)\n","print(\"all features:\")\n","mccs_a.append(evaluation(y_test,y2_predict))"],"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["all features:\n","TN,FP,FN,TP: 3803 183 347 189\n","mcc value: 0.36080377585363244\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"OUtssTj6SVr-","colab":{"base_uri":"https://localhost:8080/","height":335},"executionInfo":{"status":"ok","timestamp":1632118618106,"user_tz":-480,"elapsed":9390,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"300a1788-b11b-4d34-fca8-0dd8495d52cc"},"source":["y3_predict = knn3.predict(X_g_test_standard)\n","print(\"selected features with generated features:\")\n","mccs_g.append(evaluation(y_g_test,y3_predict))"],"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["selected features with generated features:\n","TN,FP,FN,TP: 3687 289 259 287\n","mcc value: 0.44267469605764503\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"vfMXot79fXRJ"},"source":["We drop some features to reduce the time of model training, and mcc value also gets better."]},{"cell_type":"markdown","metadata":{"id":"b5xsxFZ_FhaX"},"source":["## 6.2 Support Vector Machine"]},{"cell_type":"markdown","metadata":{"id":"mwuVvzEtu0CA"},"source":["Because we also need to standardize the data, we directly use `X_train_standard` and `X_test_standard`.\n","\n","Tune the parameters. First we focus on `C`."]},{"cell_type":"code","metadata":{"id":"Rpcaku1zd1nx","executionInfo":{"status":"ok","timestamp":1632118672114,"user_tz":-480,"elapsed":330,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["from sklearn import svm"],"execution_count":31,"outputs":[]},{"cell_type":"code","metadata":{"id":"bj2pmXlv5cfJ","colab":{"base_uri":"https://localhost:8080/","height":293},"executionInfo":{"status":"ok","timestamp":1631977655752,"user_tz":-480,"elapsed":1506031,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"578a1cc8-ec81-4890-9543-e2866c7e09a3"},"source":["Cs = [0.01, 0.1, 1, 2]\n","scores = []\n","for c in Cs:\n","  svc = svm.SVC(kernel='rbf', C=c)    \n","  svc.fit(X_train_standard,y_train)\n","  train_accuracy = svc.score(X_train_standard, y_train)\n","  test_accuracy = svc.score(X_test_standard, y_test)\n","  cv_accuracy = np.mean(cross_val_score(svc, X_train_standard, y_train, cv=5, scoring='accuracy'))\n","  scores.append([c, train_accuracy, test_accuracy, cv_accuracy])\n","\n","scores = pd.DataFrame(data=scores,columns=['C', 'train accuracy', 'test accuracy', 'cv accuracy'])\n","results = pd.melt(scores,id_vars=['C'],var_name='type',value_name='accuracy')\n","\n","sns.lineplot(x='C', y='accuracy', hue='type', data=results)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f0370523290>"]},"metadata":{},"execution_count":44},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"N9psTnajx6xf"},"source":["### Parameters tuning"]},{"cell_type":"markdown","metadata":{"id":"e8i1U_yEWvw_"},"source":["The possible optimal `C` is 1, we fix this `C` value. Then we look at `gamma`."]},{"cell_type":"code","metadata":{"id":"EKR_MamUw3Dl","colab":{"base_uri":"https://localhost:8080/","height":293},"executionInfo":{"status":"ok","timestamp":1631979856881,"user_tz":-480,"elapsed":2201136,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"0b6a68ad-efb8-4297-8dbb-c797cd88bd1f"},"source":["gammas = [0.01, 0.05, 0.1, 0.15, 0.3]\n","scores = []\n","for g in gammas:\n","    svc = svm.SVC(kernel='rbf', C=1, gamma=g)    \n","    svc.fit(X_train_standard,y_train)\n","    train_accuracy = svc.score(X_train_standard, y_train)\n","    test_accuracy = svc.score(X_test_standard, y_test)\n","    cv_accuracy = np.mean(cross_val_score(svc, X_train_standard, y_train, cv=5, scoring='accuracy'))\n","    scores.append([g, train_accuracy, test_accuracy, cv_accuracy])\n","\n","scores = pd.DataFrame(data=scores,columns=['gamma', 'train accuracy', 'test accuracy', 'cv accuracy'])\n","results = pd.melt(scores,id_vars=['gamma'],var_name='type',value_name='accuracy')\n","\n","sns.lineplot(x='gamma', y='accuracy', hue='type', data=results)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f0360561490>"]},"metadata":{},"execution_count":45},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYcAAAEDCAYAAADeP8iwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdfVyV9f3H8de543BzAEE5oICIqIAgplJZpmaDOTXXWq1oUzHZWs6y22Xhkmjpqjn7ZW3NHN3MTKlpxaqJZWVroiiGhoI3R0NF5Ua5O8Lh7pzfHwcOHA4aKkdu/Dz3OA/PdXu+X8663uf6fq/reyksFosFIYQQog1ldxdACCFEzyPhIIQQwoGEgxBCCAcSDkIIIRxIOAghhHAg4SCEEMKBursL0FVycnK6uwhCCNErjRs3zmFenwkHcKxgfn4+kZGR3VSartfX6gN9r05Sn56vr9Xpcutzvh/W0qwkhBDCgYSDEEIIBxIOQgghHEg4CCGEcCDhIIQQwoGEgxBCCAcSDkIIIRz0qfscepvMzEymTp3a3cUQQvQCjU1mvj9Tw4HT1RScrqLgdDUHTldzrraOb54agZuLqks/T8Khm5w4cYJPPvlEwkEIYcdisVBaXUf+6WoOtAmBQyVG6hvNACgVEDrAg1GB3oS416NVd30jkIRDN3n22WfZu3cv4eHh7N69Gw8PD3JycnjzzTcJDw/n9OnTnDp1itLSUn7/+98zadIksrKySE1NRa1WEx0dzZNPPtnd1RBCXIZzdY0cKLYe/FvOCA6crqa8psG2jt5TS8RALyYMG0C4vyfhAZ4M0+tw1VjPFPLz81EqFV1eNgmHbpKUlMTatWsJDAzkiy++YObMmWzZsoVbb72VQ4cOUVxczBtvvMGBAwdYtGgR48aN4/333+ejjz7CxcWFhx56iJycnA7HRBFC9CzWJqFztrOAln+Pna2xrePhomJEgCc/iQ5oDgEvIgI88fFw6ZYyOzUcli1bxp49e1AoFCQnJxMTE2NbtnbtWjIyMlAqlURHR7N48WJqamp48sknKSsrw83Njeeffx4/Pz9mz55NTU0N7u7uACxatIjo6GhnFv2Kue2223j55ZeZOXMm2dnZPPTQQxw6dIgbbrgBgPDwcIqLizl8+DClpaUkJSUBUF1dzcmTJyUchOhBLBYLJdV1zQf/jpuEVEqFtUkoyJtfjAsiPMCTiAAvgnzcnHIGcKmcFg7Z2dkUFhaSnp6OwWAgOTmZ9PR0AIxGI2lpaWzevBm1Ws28efPIzc0lNzeX4OBgVq5cya5du1i5ciV//OMfAfjTn/7EiBEjnFXcbhMREUFZWRl79+5l+PDhaLVaAMxms916Go2GsLAw1qxZ0x3FFEK0075JKP9UFQeKq6lo0yTk76UlPOD8TUI9mdPCISsri7i4OADCwsKorKzEaDSi0+nQaDRoNBrb2UBtbS3e3t58//33tl/MsbGxLFmyxFnF63ZKpZLGxkYApk2bxrPPPsujjz5qW56Tk8NvfvMbCgoKGDRoEKGhoZw4cYIzZ87Qv39/Vq5cyd13342/v393VUGIq0L7JqH8U9UcKK7i+Nla2zotTULTmpuEIgZ6Ee7ffU1CXcFp4VBWVkZUVJRt2tfXl9LSUnQ6HVqtlgULFhAXF4dWq2XGjBmEhoYyYsQItm7dytSpU8nOzubkyZO27VeuXEl5eTlhYWEkJyfj6urqrKJfEWFhYezfv59ly5Yxb9483njjDcaPH29brtPpuP/++ykqKiI5ORk3NzeSkpL4zW9+g4uLCyNHjkSv13djDYToW9o2CRWcqrL1DRwudWwSignqx13jgokYaO0XCOzXs5qEusIV65C2WCy290ajkVWrVrFp0yZ0Oh2JiYkUFBRw5513cuDAAe655x6uu+46fH19AZgzZw7h4eEMHjyYlJQU1q5da2t7bys/P99u2mQyOczrSV577TUA/vWvf3HLLbdw4MABAEpLSwkICGDGjBm2dfPz8xkzZoztzAqgoKDgyhbYCXr6d3SxpD49n8lkImfvPgor6jlaXs/3La+KeqrrWptz+7upCPV14afhngzxcWGIjwvB3hpcVC2XjTYCZzEWn+VAcbdUBXDed+S0cNDr9ZSVldmmS0pK8PPzA8BgMBAcHGw7+MfGxpKXl0dERASpqakAnDt3ji1btgAQHx9v288tt9zCp59+2uFntn/gRW94qMcf/vAHjh8/zl//+ld0Oh0Afn5++Pj49Mr6XKy+ViepT8/S2GTmaJn9VUJ5x89w2thoW8fDRUV4gCe3jtYTEeDZ3EHsST/33tEk5KyH/TgtHCZMmMArr7xCQkIC+/btQ6/X2w5+gYGBGAwGTCYTrq6u5OXlMXnyZLZu3cq3337Lww8/TEZGBhMnTsRisXDvvfeycuVKvLy82LFjB8OHD3dWsa+45557zmHegw8+2A0lEaL3slgsFFfV2e4TsDUJlRipb2ptEho6wIMRA7TMunGo7VLRvtgk1BWcFg5jx44lKiqKhIQEFAoFKSkpbNy4EU9PT+Lj40lKSmLOnDmoVCrGjBlDbGwsJpOJtWvXctddd+Ht7c2KFStQKBTcddddzJ07Fzc3N/z9/eXgKcRVzFjXaAsA2+Wi7a4SCvByJTzAk4nDBxAe0HqVkFatav6l3Xd+YDqLU/scHn/8cbvpiIgI2/uEhAQSEhLslru6uvL666877Gf69OlMnz7dOYUUQvRIjk1C1iA4Ud56lZBOq2aEv45p0QN7ZZNQTyZ3SAshulX7JqGC5pehgyaha4L7kXBtMBEBXoQHeBLk44ZCIU1CziDhIIS4YlqahNoGwYHT1VTWOjYJTRoxwHo24O9FmN4Drbrn3zjWl0g4ONnFDMu9dOlS5syZQ3BwsJNLJYRzNdg1CbUGQfsmofAAT2bENDcJNd9BLE1CPYOEgxNd7LDcixcvdnKJhOhaFouF01Um2xnAgfM0CYX5eTBmsA/3XDfYFgLSJNSzSTg4Ucuw3K+++ioWi4Xjx49z4sQJ3nrrLZ566imKi4upqanhwQcfZMqUKcyePZunn36azMxMqqurOXr0KMeOHSM5OZnJkyfb9tvY2MiiRYsctt+/fz+pqakoFArGjBnDokWLOpzX8jkjRozgnXfeoby8nOuuu4433niDmpoaFi1aRHZ2NpmZmZjNZiZPnswDDzxAVVUVjz/+OEajEU9PT5YvX87tt9/ORx99ZDfk+KuvvtqNf3XhLNWmBg4WVzuMLNq2SWigtzQJ9RVXTThsyDnBe7uOd+k+74oN5o5xQedd3jIs9wMPPMArr7xCQ0MD7777LmfOnOGmm27i9ttv5/jx4zz00ENMmTLFbtvTp0+zevVqvv76a9avX28XDpWVlR1u/9xzz5GamkpERARPPPEERUVFHc47n4MHD5KZmYmLiwvZ2dm8++67KJVKfvSjHzF37lzS0tK46aabmDNnDm+99RY7duwgPj7eYchx0bu1bRL6X95ZzmTvJP9UNUUV528SigiwjiXk7a7pxpKLrnTVhENP0DJkuZeXF9999x3p6ekolUoqKioc1h07diwAAQEBVFdX2y073/ZHjx61XS784osvnnfe+YSHh+PiYm3vdXV1ZdasWajVasrLy6moqGD//v089NBDAMydOxeAoKAghyHHRe/QUZNQ/qkqjpSea20SUkCYXsfYEB9+ef1g2+Wigf2kSaivu2rC4Y5xQRf8lX8laDTWX1Uff/wxlZWVvPvuu1RUVHDnnXc6rKtWn/+rOd/2SqXjowI7mtdWy8iwgC0YioqKeOutt/jggw/w8PCwnQ2oVCqHocTPN+S46FnaNgkVnGq9b6DK1Pr9tzQJ3RzeOoxE/ZkTjI4e2Y0lF93lqgmH7tB2WO62ysvLCQoKQqlU8tlnn1FfX39R+z3f9mFhYezZs4fRo0eTnJxMUlJSh/N0Oh2lpaWMGDGC3bt3OwxHUl5ejq+vLx4eHuzbt4+ioiIaGhqIjo5m+/btxMTEsH79erRaLbfffnuHQ46L7tHSJJR/yn4YibZNQp5aNSMCPJk5elBzCJy/SSi/Qs4OrlYSDk7UdlhuT09P2/wf//jHzJ8/n9zcXO644w4CAgIuqhP3fNsvXryYZ555BoBrrrmGsLCwDufdfffdPPvss4SEhDB48GCH/UdGRuLh4UFCQgLjxo0jISGB1NRUXnnlFZ544glmz56Nh4cHy5cvB6x3sLcfclw4l61J6FS13VPHDKVGGpqsIyCrlQrC/HSMkyYhcQkUlrZjafdiHT1PubePKNleT63Phg0bKCoqYuHChRe9bU+t06VyRn2qTA0cbHN1UEdNQoOam4RaBpMLD/AkzE+Hi/rCzYo/pK99P9D36tQVo7J29LhhOXMQl6XtkOPi8jQ0mTlSes5hZNH2TULhnWwSEuJySDiIy9LRkOPiwiwWC6cqTW3uFfjhJqHIgdYgGOTtKk1C4oqQcBDCido2CbU9I+ioSejmcH1zCHgydMDlNwkJcTkkHIToIicravnqiJGMwgJbCHTUJPTTawbZ+gZG+Hvi7SZNQqLnkXAQ4jKYzRb+e7iMNVmFfFFQjNkCamUpw/Q6Yof48KuAwba+AWkSEr2JhIMQl6Cipp73d53gnR2FFJ6pYYDOhd/dPIyRulriro+RJiHR60k4ONnFDNndYufOnQwdOpT+/fs7qVTiUu09UcGarEIy9pykrtHMdUN8eezH4fwkKgAXtZL8/HwJBtEnODUcli1bxp49e1AoFCQnJ9vGFgJYu3YtGRkZKJVKoqOjWbx4MTU1NTz55JOUlZXh5ubG888/j5+fHwUFBbYbucLDw0lNTXVmsbvMxQ7Z3WLDhg3MmzdPwqGHMDU08fHeU6zZXsie4xW4u6i4c1wQs8aHEDnQq7uLJ4RTOC0csrOzKSwsJD09HYPBQHJyMunp6QAYjUbS0tLYvHkzarWaefPmkZubS25uLsHBwaxcuZJdu3axcuVK/vjHP7J06VJbuDz22GNs3brVbpTSnqrtkN1z584lOTmZyspKmpqa+MMf/kBERASvv/46n332GUqlkilTpjBq1Cg+//xzDh06xCuvvMKgQYMA6xhIK1asoLa2VobpvkIKz5xj7Y5jvLfrOBU1DQzT63j2tihuHxOIp6t0Iou+zWnhkJWVRVxcHGAdRqKyshKj0YhOp0Oj0aDRaKipqcHd3Z3a2lq8vb35/vvvueGGGwCIjY1lyZIl1NfXU1RUZDvrmDJlCllZWRcfDrnr4Nt3urSOjJkF19xz3sVth+z+61//ysSJE/nFL37B4cOHWbp0KW+++SZvvPEG33zzDSqVinXr1jFhwgQiIyN5+umnbcEA1mG6r7nmGhYsWCDDdDtRk9nCVwdKWLO9kK0HS1EpFEyNCmDW+BDGD/WVDmVx1XBaOJSVlREVFWWb9vX1pbS0FJ1Oh1arZcGCBcTFxaHVapkxYwahoaGMGDGCrVu3MnXqVLKzszl58iTl5eV4ebWeuvfv35/S0tIOPzM/P99u2mQy2eZ5nzyJd825Lq1j5cmTVGrzz7u8sLCQqqoq8vPz+eabb6isrGTdunUA1NfXk5+fz/XXX89dd93FpEmTmDhxIvn5+Zw7d44jR47Q1NRk21djYyMFBQXcdtttKBQKysrKyM/P5/Dhw1gsFvLz87n33nupqqrqcF7bfZ4+fZqqqioKCwsJDAzEYDAAUFFRwR133IFSqeTMmTPs2rWL7OxsfvnLX9rKCtieSzFs2DC+/vprpk6d6vC376y231F3qjA1sflQNZ8cqKLkXCP93VT8KsaHn4zwpL+7GupLKCgo+cH99JT6dJW+Vh/oe3VyVn2uWId02yGcjEYjq1atYtOmTeh0OhITEykoKODOO+/kwIED3HPPPVx33XX4+vpecD/ttR9fxG7MkchI4LEuqUsLD2DQBZZXVVXh5eVFZGQk/fr144knnmDMmDF267z88ssYDAb+85//8Nxzz/H+++/j4eHB0KFDGTFihG29Dz74gNraWj744APbMN2RkZFoNBqHenc0r+0+d+zYgVarJSQkBF9fXyIjIykqKmLTpk12w3QPGzYMb29vQkJC7PYXGRnJG2+8QUNDA6NGjWL06NGX/DfsznFuLBYLu49V8M72Qj7Ze4r6JjM3DO1P6g0hxI30R6O6+I5lGben5+trdeqKsZU64rTLKvR6PWVlZbbpkpIS/Pz8ADAYDAQHB+Pr64uLiwuxsbHk5eXh4uJCamoq69at47777sPd3R1fX1+7h+EUFxej1+udVewu1XbI7tGjR/P5558DcPjwYd58802qq6t59dVXCQsL44EHHsDb2xuj0YhCobA7awDrMNr+/v7nHaYbIDk5GYPB0OG8lmG6AXbv3u1Q1h8aphtg/fr1fPDBBwC2YbpnzpzZ1X82p6upb2R99jFufeUb7nhtG5/vL+aX1w/m80cnse6+8UwbNfCSgkGIvsRp/wVMmDCBzMxMAPbt24der0en0wHYmjJMJhMAeXl5DBkyhK1bt/J///d/AGRkZDBx4kQ0Gg1Dhw5l165dAGzevJmJEyc6q9hdqu2Q3bNmzeLYsWP88pe/5A9/+AOxsbF4enpSXl7OnXfeyZw5cxg9ejT9+vXjuuuuY+HChRw6dMi2rx//+Mfs3LmTxMRE3Nzc7Ibpfv7557nnnnvw9va2DdPdfl7LMN333Xdfh+HadpjuTz/91DZMd2JiIt9++y2zZ8/mq6++Ij4+HrAO03369OleNUy3odRI6r/3cf2yLTy58TuazBaW3h7N9uQf8cxPoxim9/zhnQhxlXDqkN3Lly9n165dKBQKUlJS2L9/P56ensTHx7N+/Xo2btyISqVizJgxPPHEE5hMJhYuXEhFRQXe3t6sWLECT09PDh8+zJIlSzCbzYwePZqnnnrK4bNkyO4r63KG6W7L2XVqbDLzeX4J72wv5JvDZWhUCqZFD2TODSGMC/Hp8g7mnvQddYW+Vh/oe3XqlUN2P/7443bTLc8yBkhISCAhIcFuuaurK6+//rrDfoYNG8a7777rnEKKi9YbhukuqTaxPvs47+44xukqE4O8Xfn91HDuig3Gz1MeZSrED5E7pMVF66nDdFssFrKPnmXN9kI25Z2m0Wxh4vAB/PFn0UwJ90Mt/QhCdJqEg+j1jHWNfLD7BGu2F3Kw2IiXq5q5Nw7hV+NDCB3g0d3FE6JXknAQvdbB4mrWZBWycfcJztU3ER3oxYt3xDBz9CDcXFTdXTwhejUJB9Gr1Dea2bz/NGuyCtlx9CwuaiW3xgxkzg1DGB3kLXcwC9FFJBxEr3CqspZ1O46xbudxSqvrCPZ146lpEfwiNhhfD5fuLp4QfY6Eg+ixLBYL2wxnWJNVyGf5xZgtFqaE65k9PoTJI/xQKuUsQQhnkXAQPU5lbQMbcqwP0jlSeg4fdw2/nhjKrOtDCPZ17+7iCXFVkHBwooaGBp588kmKiorQarW8+OKL3H///fz1r39l0KBBFBUV8eCDD7Jx40bbNgUFBaSmpqJWq1Eqlbz88sv069eP1atX89FHH+Hu7s6jjz7K+PHjWb16NZmZmSiVSh599FGCgoJYuHChbX8///nPWblyJa+++ioajYaKigr+9Kc/8dhjj1FTU4PJZOLpp58mJiaG//3vf6xYsQKVSsX06dMJDQ3l448/5s9//jNgvbdhypQp/OhHP3La32vfyUre2V7Ih9+epLahiTGD+7HirtFMHzUQV410MAtxJV014ZBhyOCDQx906T5vH347Pw376XmXf/jhhwwYMIC//OUvfPLJJ2zZsoW4uDi+/PJLfvWrX7FlyxZ+/OMf221z5swZnn76aUaOHMnLL7/Mv//9byZOnEhmZiYvvPACOp2O119/nYCAADIzM3nvvfc4fvw4r7/+OvPnzz9vWby9vfnjH//I0aNH+cUvfkFcXBxZWVmsXr2alStXkpqayvr16/H29uZ3v/sdd911F8uWLaOurg6NRsPu3btZsmRJl/3tWtQ3Wfjw2yL+mfU9u49V4KpRctvoQGbfEEJ0oHeXf54QonOumnDoDvv27bM9n2LGjBkAHDp0iOeff94WDi1PuGvRv39/li9fjslkoqSkhJkzZ7J//35Gjx6NUqkkJCSEpUuX8umnnzrMO3HixHnL0vI8jAEDBvC3v/2NtLQ06uvrcXd35+zZs2i1WtsouKtWrQLg5ptvZuvWrfj5+REbG4uLS9d1/J6uNPF21ve8u72QSpOZ0AEePH3rSO4cG4S3uzxIR4judtWEw0/DfnrBX/nOoFKpMJvNdvOGDx9OSUkJp06dorq6mtDQULvlS5cu5Te/+Q2TJk0iLS2NmpqaDvfT0bz2l3G2jAgL1mG8Ad5++238/f3585//zHfffceLL76IUql02BfAz372M1avXk1gYGCXPdDnwOlqXv/6CBl7imgyW7g+yJ3f/TiaCWEDpINZiB5ExhNwolGjRtmGu/7yyy/5+9//Dlh/kb/00kvccsstDttUVFQwePBg6uvr2bp1Kw0NDURFRbF7926ampooKytjwYIFtnmNjY22eTqdjjNnzmCxWCgtLeX48eMO+y8vL2fw4MEAfP755zQ0NODj40NTUxPFxcVYLBZ++9vfUlVVRWRkJMXFxezdu5drr732kv8OFouFLMMZ7n0zm6n/9zWffneKX10fwtbfT2HJLQFMHC5XHgnR01w1Zw7dYfr06Wzbto1Zs2ahVqt54YUXAIiPjychIYGMjAyHbWbNmsWCBQsIDg5m9uzZPPvss0yfPp3bbruN5ORk3NzceOSRRwgKCuK2225j1qxZWCwWHnnkEby9vbnxxhu54447iIiI6HCkxttuu41FixaxadMmfvWrX/Hxxx+zYcMGUlJSbCOsTps2zfb0vQkTJnDu3LlLurmsscnMpn2nef3rI+w9UUl/DxcejR/B7PEh+DTfm5BffNG7FUJcAU4dsvtKkiG7u57FYuHee+8lNTWVkJCQTm9XW9/E+znH+cd/j3LsbA1D+rvzm0lDuWNskMNVR/Id9Wx9rT7Q9+rUK4fsFr3XiRMnWLhwIT/5yU86HQxnjHW8nVXImqzvKa9pYMzgfiRPjyB+ZAAqaTYSoleRcBAdCgoKsrv/4kK+LzvHP745wvu7TlDXaCYuUs9vJ4cR64SH6QghrgwJB3HJvj1WzutfH2HTvtNolEpuHxPIbyaFyuM2hegDnBoOy5YtY8+ePSgUCpKTk23X2gOsXbuWjIwMlEol0dHRLF68mOLiYpKTk6mvr8dsNvPUU08RHR3NLbfcQkBAACqVtb16+fLl+Pv7O7Po4jzMZgtfHihh1ddHyD56Fk9XNfdPDuPeG4eg93Lt7uIJIbqI08IhOzubwsJC0tPTMRgMJCcnk56eDoDRaCQtLY3NmzejVquZN28eubm5ZGZm2q7k2b17Ny+99BJpaWkArF69Gg8PeXBLd6lrbOKjb0/y+n+PcLjEyCBvV/4wI5KE6waj08oJqBB9jdP+q87KyiIuLg6AsLAwKisrMRqN6HQ6NBoNGo2Gmpoa3N3dqa2txdvbGx8fHyoqKgCoqqrCx8fHWcUTnVRZ28C7O47x5v+OUlJdR0SAJ/939zXMiBmIRh67KUSf5bRwKCsrIyoqyjbt6+tLaWkpOp0OrVbLggULiIuLQ6vVMmPGDEJDQ5k7dy533nknH374IUajkXXr1tm2T0lJoaioiHHjxvHYY49JR6eTnayo5Y1vjrJ+53GMdY3cNGwAy38xmonDB8jfXoirwBVrD2h7O4XRaGTVqlVs2rQJnU5HYmIiBQUFfPHFF0ybNo358+fz5Zdf8sILL/Dqq6+ycOFCJk6ciLe3NwsWLCAzM5Of/OQnDp+Rn59vN20ymRzm9WZXoj5Hz9axYV8lXx01YgEmDfHgjih/hvXXQlMZBQVlXfp58h31bH2tPtD36uSs+jgtHPR6PWVlrQeSkpIS/Pz8ADAYDAQHB9sGeouNjSUvL4/du3fz8MMPA9Y7c1NTUwHrGD8tJk2axMGDBzsMh/Y3gsjNLp3TMrzF378+wtcHS3F3UTHnxiHMmxDq9OcnyHfUs/W1+kDfq1NX3ATXEac1Gk+YMIHMzEzAOjqpXq9Hp9MBEBgYiMFgwGQyAZCXl8eQIUMICQlhz549AOzdu5eQkBCqq6tJSkqivr4egJ07dzJ8+HBnFfuq0thkJmPPSWa++g2//McO9p+s5PEfj2Dbk7eQMjNKHqwjxFXMaWcOY8eOJSoqioSEBBQKBSkpKWzcuBFPT0/i4+NJSkpizpw5qFQqxowZQ2xsLIMHD2bx4sVs2rQJgMWLF+Pp6cmkSZO4++670Wq1jBw5ssOzBtF5NfWNvLfzOP/45ignymsZOsCDZbeP4udjA+WhOkIIwMl9Do8//rjddEREhO19QkICCQkJdsv1ej2rV6922E9iYiKJiYnOKeRVpMxYx9vbvmfN9kIqahoYF+LD07eOJD7SX0ZFFULYkQvUrwJHy86x+r9H+FfOCRqazMRF+vPbSUOJHeLb3UUTQvRQEg59WE5hOa9/bWDz/mI0KiV3jA3k1xOHEuan6+6iCSF6OAmHPsZstrCloIRVWw3sKizHy1XN724OI/HGIeg9ZXgLIUTnSDj0EaaGJj78tojV/z2CofQcgf3cWHLrSO6+NhgPGd5CCHGR5KjRy1XWNPDOjkLe2vY9pdV1jBzoxcsJ1zB9lAxvIYS4dBIOvVRRy/AW2cc4V9/ExOEDeOmua5gwrL8MbyGEuGwSDr1F/TkUjbXsP1nF618b+PfeUwDMjBnIbyYNJWqQdzcXUAjRl0g4dJf6GjhXCjVlcO5Mm/el1um278+VQmMtEUCNRctjePO4j57+/kG4eQRAgR8c14PODzz0oNODxwBw7QdyFiGEuAQSDl2lvqb5gN78sh3cy6DmTOv7lmUNNR3vR+0K7gOsB3ePATAgnKO17vyroBYXlYLJQQpGetfhYioD43EozrHu32J23JfKBTz8rC+dvt37NmHi4QfuvqCUu6OFEFYSDufTUNt8MC9tPriXtfl13z4AzkDDuY73o9I2H5T7Ww/6A0a0HvhtIeAH7v2t7110dr/212cfI/mD7xgd3NcV4kcAACAASURBVI8nb/DmmjHRjp9hbmoNIGNJm39LrOU0loCxGE7nWZeZGxz3oVBay+MQIm3CxKPNcpWmi/7QQoieSMKhMAu+fcf+l/65sh842Lc5uPcfZn9w9/Cz/+Xf7mDfWRaLhb99ZeDPmQeYPMKP12aNpdBwqOOVlSrrQVunB/+ojtdp3THUljfXs6RdmJS2vj9rAKO1OatDbj6tZx225qy2zVptAkbjdtH1F0J0LwmHk7vhyJetB/f+w+wP7u4D7H/5az2d3o5vNlv44yf7efN/3/Ozawbx51+M7rrLUhUKaxOSuy/4jbjwuhYL1BubA6PUPkzaBsqpvdZ/66o63o+LZ3NwtGvW8vTHrVYH9SHgIiPACtGTSDjcsMD66iHqG838/l97+Cj3JPdOGMLTM0Z236B4CoU1DLWe4Dv0h9dvMDUHR0m7MGlzlnLmMBRug9qzAAwB+EIFfhEw6BoYeA0MGgMB0XLGIUQ3knDoQWrqG5n/zm62Hizl91PD+d3NYb3rngWNK/QLtr5+SFMDVJ/i+M5PCVaVwclcOJgJuWutyxUtgTGmNTQkMIS4YiQceojyc/Xc+9ZO9p6o4PmfjyLhusHdXSTnUmmg32CMQZOh5SlWFgtUFVmD4uS3cCoXDm6C3HesyxUq0Ec2n100n2H4R0lgCOEEEg49wMmKWua8kc2xszW8NmscU6MCurtI3UOhAO8g6yvyVus8iwUqT1iDoiU0Dv6nXWCMhEGjm0NjbHNgyCCDQlyOToXDCy+8wK233kpU1A9cCSMu2uGSauakZVNtauSf865j/ND+3V2knkWhaG2qipxpndcSGC1nFydzoeBT61VnAEo1+EU2n11cAwPHSGAIcZE6FQ4jR45k9erVFBUVcfPNN/PTn/6U4OBOtCuLC/r2WDn3vrUTtVLJ+t+OlyEwOqttYIz8qXWexQKVx+2bpAo+gW/XWJcr1W2apJr7MfQSGEKcT6fCYebMmcycOZOGhga2b9/Oo48+ilKpJCEhgZ/97Gfn7TRdtmwZe/bsQaFQkJycTExMjG3Z2rVrycjIQKlUEh0dzeLFiykuLiY5OZn6+nrMZjNPPfUU0dHRbNu2jRUrVqBSqZg0aRILFvScq4su1daDpdy/Jgc/Ty1rkq4jpL9Hdxepd1MooN9g66ttYFQcaz27OJULBR87BsagMa39GP7RoNZ2Xz2E6CE63eeQm5vLJ598QnZ2Ntdeey3Tpk1j27ZtPPzww7z88ssO62dnZ1NYWEh6ejoGg4Hk5GTS09MBMBqNpKWlsXnzZtRqNfPmzSM3N5fMzEzi4+NJSEhg9+7dvPTSS6SlpfHcc8+RlpaGv78/s2bNYurUqQwbNqzr/gpX2Ee5RTz+/h6G6T15+95r0XvJr1enUCjAJ8T6GnmbdZ5dYHxrDY38f8Puf1qXKzWtgdFylZR/lASGuOp0KhymTp1KREQEt912G4sWLUKttm42btw4fvvb33a4TVZWFnFxcQCEhYVRWVmJ0WhEp9Oh0WjQaDTU1NTg7u5ObW0t3t7e+Pj4UFFRAUBVVRU+Pj4cP34cb29vBg4cCMDkyZPJysrqteHw1v+Okvrxfq4d4svqObF4u8kwFFfUeQOjsPXs4uS3sP8j2P22dblSA/4j2zVJjZTAEH1ap8IhPT2dY8eO2ZqFsrKyGD9+PAqFglWrVnW4TVlZmV0Htq+vL6Wlpeh0OrRaLQsWLCAuLg6tVsuMGTMIDQ1l7ty53HnnnXz44YcYjUbWrVtHaWkpvr6+dvs5fvx4h5+Zn59vN20ymRzmdReLxcKa3HLW7a3ghmB3Ft3oxcnvD3PyIvbRk+rTVXpUnZThEBgOgXeDxYLm3Clcy/NxPVuAa3kBbt9tRNUcGBalGpN3GCafSEy+EZh8IqzTDU09pz5doEd9P12kr9XJWfXpVDgsW7YMvV5vC4edO3fy4Ycf8sILL3T6gywWi+290Whk1apVbNq0CZ1OR2JiIgUFBXzxxRdMmzaN+fPn8+WXX/LCCy8wb968Tn9GZMv18s3y8/Md5nWHJrOFP3yYx7q9FdwdG8zS26NRX8JwGD2lPl2pZ9dpJPCj1kmLBcq/h1O5KE5+i9vJXNxOfglHPrQuV7lg8hyCa1CM9QY+v3Drv76hvXagwp79/Vyavlany61PTk5Oh/M7FQ4nT57kxRdftE0vXLiQ2bNnX3AbvV5PWVmZbbqkpAQ/Pz8ADAYDwcHBtjOC2NhY8vLy2L17Nw8//DAAEyZMIDU11WE/xcXF6PX6zhS7RzA1NPHw+lw27TvN724O4/dTw3vXXc+ilUJhPdD7hkLU7dZ5FguUH7U1STUeyYbj2ZD3r9btlBrrmF1+4W1eEdZ50jQleqhOhYNCoeCrr75izJgxmM1mtm/fbut3OJ8JEybwyiuvkJCQwL59+9Dr9eh0OgACAwMxGAyYTCZcXV3Jy8tj8uTJhISEsGfPHqKjo9m7dy8hISEEBQVhNBo5ceIEAQEBfPnllyxfvvzya34FVJsauO+fOWQdOcMfZkTy64mdGJ9I9C4KhXXcKd+hEP1zjrf8iqszQtlB66u0AEoPwOm9kJ/R+uwNhdK6nV+EdSj3lrONASNkIELR7Tp9E9xLL73En//8Z5RKJTExMTz//PMX3Gbs2LFERUWRkJCAQqEgJSWFjRs34unpSXx8PElJScyZMweVSsWYMWOIjY1l8ODBLF68mE2bNgGwePFiAJ555hkee+wxAKZPn05oaOjl1PmKKK2uY+6b2Rw4Xc1Ld4/m9jFB3V0kcSVpdRA41vpqq6HWOvhg6YHmV3NwHNwE5sbmlZovy/WLsI6c6xfRGiCuXle8KuLqpLC07QzopIaGBlJTU3nuueecUaZLkpOTw7hx4+zmdVfb4vGzNcxO28HpKhOv/WocUyK6phmsr7WVQt+r0yXXp7Eezh6BsnahUXYImupa1/MKtD/LaPnX3ff8+74Mfe37gb5Xp67oc2h/7IROnjm8//77rFy5kvLyclxcXDCbzdx8882XXJi+LP9UFXPeyKa+0czaX49nXIhPdxdJ9AZqF9BHWF9tNTVaL7NtGxilBdbLbNs+atZDb9+f0fKvh588R1xckk5fyvr555/z61//mjVr1rBlyxZOnDjh7LL1OtlHz5L09k48XNS8f/8NjPD37O4iid5OpYb+YdZXxPTW+WazdbgQW59Gc3Dsfc/+oUtuPo59Gn4R4DVIQkNcUKfCQavVotVqaWhowGw286Mf/YjZs2eTmJjo7PL1Gp/tL+aBd3cT6OPGmqTrCewnw0gLJ1IqW2/mGx7fOt9igepTjn0a+f9uvakPrE/n8+ugecp7sHXf4qrXqXAYNWoU77zzDjfddBOJiYkEBARgMpmcXbZe471dx3lq43dED/LizXuvw9fDpbuLJK5WCoX1rMBrEIRNsV92rsz+LKO0AA5/3vqAJQC1GwwYbgsLXZ0O/DTgM8R6FiOuGp36tufNm0e/fv1wcXHh+uuvp7y8nBtvvNHZZesV/r7VwPP/KWDi8AH8fdY4PLTyH5DooTwGgMdNMOQm+/m15VB60D40CrfBd+8RDPA/QOXS5l6Ntjf4hVn7S0Sf06kj2aOPPso771jHyr/22mudWqDewmy28PymAl7/+gi3xgzkL3eNRqtWdXexhLh4bj4w+Hrrq626ao7u3Eyorq41OE5+C/s+BJovclSomu/VCG+95NYv3Hr2IU/o69U6FQ5+fn4kJCQwatQoNJrWYQCeeOIJpxWsJ2toMvPkhu/YsPsEiTeEkDIzCqVSOvdEH6P1xNR/ZOtjXFs01FovsbX1aTQHx4H/gKWpeaXmAQ7b92kMGAFauVCjN+hUOEyaNMlh3tU6BERtfRML3t3NFwUlPBo/ggdvGXbV/i3EVUrjBgNjrK+2Guus92q0bZ4qPQCGL6CpvnU9ryDH5im/EdYzGNFjdLqBXA6AUFnTwLy3d7L7WDnP/SyaWeNDurtIQvQcaq31WRj6dmcaTY3WAQvbd4YXboPG2tb1dP4dhEaEta9EXHGdCoeDBw/a3jc2NrJnzx6GDx/Oz372M6cVrKc5XWki8Y1sjpad46+/HMv0UQO7u0hC9A4qNQwYZn1F3to632yGymOON/jlroP66tb13Hwdm6f8IsAzQO7VcKJOhcOiRYvsppuamli4cKFTCtQTHSk1Mjstm4qaet6891omDJNfMkJcNqXSeomszxAYMbV1vsUCVScdm6f2fQCmitb1tF7t7gpvDg6vILlXowt0Khxqa2vtpktLSzly5IhTCtTT7D1Rwdw3d6IA1t93A6OCvLu7SEL0bQoFeAdaX8PaPU/jXKljaBzcDN++07qexr3j8ad8hoBSrijsrE6Fw4wZM1AoFFgsFhQKBZ6enhf1EJ7e6n+Hy7jvn7vo5+7CmqTrGOqn6+4iCXH1UihAp7e+QttdJFNz1rF56vv/wt71reuotDBgOIO0A6Hkujb3agzttQ9jcqZOhcMXX3xBXV0dWq31wSTV1dV4evbty9E+2XuKR9JzCR3gwT+TrsPfy7W7iySEOB93Xwi5wfpqy1TZfNlta2e428k8OPZZ6zpKtfVmvvZ9Gv2Hgebq/e++U+Hwz3/+k23btvH3v/8dgN///vfceOONzJkzx6mF6y5rthey5KM8xg32IS3xWrzd5VeFEL2SqzcExVpfzQz5+USGDW53r8YBKNkPBR/bP4zJZ0jH92q4eHRPfa6gToXDp59+yrvvvmubfu2117jnnnv6XDhYLBZe3nKI//v8ED+K0PPqL8fi5iJtlEL0OS4eMOga66utBhOcNbTr1zgIhz4Dc0Pret6DO+gMH2ENoz6iU+HQ2NhIVVUV/fr1A6wd0n1Nk9nCMxn7WLO9kDvGBvH8HaPQqOSKByGuKhpX8I+yvtpqaoCzR+1Do+yAtV+jsc0gpJ4D251ltDxXo/+VrUcX6FQ4PPLII9x9991otVrMZjNms5klS5b84HbLli1jz549KBQKkpOTiYlpvaNy7dq1ZGRkoFQqiY6OZvHixbz22mts27YNALPZTFlZGZmZmdxyyy0EBASgUll/xS9fvhx/f/9LqW+H6hqbePS9PXyy9xT3TRrKU9Mi5KY/IUQrlaZ5iPMR9vPNTR08jOkA7F4DDeda13Mf0MG9GuHWG/966LGmU+EwYcIEMjIyOHfuHEqlEpVK9YMd0tnZ2RQWFpKeno7BYCA5OZn09HQAjEYjaWlpbN68GbVazbx588jNzWX+/PnMnz8fgA8++IAzZ87Y9rd69Wo8PLq+nc9Y18j9a3L45nAZT02L4LeTw7r8M4QQfZSyeeBB36EQPq11vtkMVUX240+VHYTv/gV1la3ruXp30KcRDt5B3R4anQqHt99+m6ysLFuH9P333/+DHdJZWVnExcUBEBYWRmVlJUajEZ1Oh0ajQaPRUFNTg7u7O7W1tXh7t7bVNTY2sm7dOv75z39eTt06ZcXmg2QdOcOf74zhF7HBTv88IcRVQKmEfsHW1/C41vkWCxiLHfs0Cj6F3W2Ody66Du7VGAH9Qq7YvRqdCof//Oc/F90hXVZWRlRUa7udr68vpaWl6HQ6tFotCxYsIC4uDq1Wy4wZMwgNDbWtu3nzZm666SZcXVsvI0tJSaGoqIhx48bx2GOPdVmzz+wbQvj52ECiA/tOR5IQoodSKKzDfngGwNCb7ZedK2vTn9H8fI0jX8Ke1mMvale7hzExIBx1jSfQbjyrLnDFOqQtFovtvdFoZNWqVWzatAmdTkdiYiIFBQVERFgfrr5hwwZSU1Nt6y9cuJCJEyfi7e3NggULyMzM5Cc/+YnDZ+Tn59tNm0wmh3kdUQH5VScvuk5XWmfr05v0tTpJfXq+nl0nX9DdaH0Nsc5R1lejrfoel6qjaKu+R1t1FBfDf3H57n0AwpQaCrSbsai79vkZnX7YT0uHtMVioampiaSkpAtuo9frKSsrs02XlJTg5+cHgMFgIDg4GF9fXwBiY2PJy8sjIiKCmpoaTp8+TVBQkG3btgP8TZo0iYMHD3YYDpHtxp3Pz893mNeb9bX6QN+rk9Sn5+uddbrOcVadEcoOcsxwkIhRYy95zzk5OR3O79S1mp6enoSHh9PUZH2Qh5eXl63/4XwmTJhAZmYmAPv27UOv16PTWYefCAwMxGAw2J5DnZeXx5AhQwAoKChg6NChtv1UV1eTlJREfb11PPidO3cyfPjwzhRbCCH6Lq0OAsdS6zfaKbvv1JnDc889xyOPPMJf/vIXUlJS+Oyzz7jmmmsuuM3YsWOJiooiISEBhUJBSkoKGzduxNPTk/j4eJKSkpgzZw4qlYoxY8YQG2u9g7G0tNR2RgHWYJo0aZLtzGXkyJEdnjUIIYToOp0KB1dXV8aPH49GoyE6Opro6GiSkpKYMmXKBbd7/PHH7aZb+hQAEhISSEhIcNhm6tSpTJ061W5eYmIiiYmJnSmqEEKILtCpcHBzc2PLli0EBQWxYsUKgoODOXXqlLPLJoQQopt0qs9h+fLlhIWFsWTJElxcXDhw4AAvvPCCs8smhBCim3TqzEGn09k6kx944AGnFkgIIUT3k5HlhBBCOJBwEEII4UDCQQghhAMJByGEEA4kHIQQQjiQcBBCCOFAwkEIIYQDCQchhBAOJByEEEI4kHAQQgjhQMJBCCGEAwkHIYQQDiQchBBCOJBwEEII4aBTQ3ZfqmXLlrFnzx4UCgXJycnExMTYlq1du5aMjAyUSiXR0dEsXryY1157jW3btgFgNpspKysjMzOTbdu2sWLFClQqFZMmTWLBggXOLLYQQlz1nBYO2dnZFBYWkp6ejsFgIDk5mfT0dACMRiNpaWls3rwZtVrNvHnzyM3NZf78+cyfPx+ADz74gDNnzgDWZ1inpaXh7+/PrFmzmDp1KsOGDXNW0YUQ4qrntGalrKws4uLiAAgLC6OyshKj0QiARqNBo9FQU1NDY2MjtbW1eHt727ZtbGxk3bp1zJo1i+PHj+Pt7c3AgQNRKpVMnjyZrKwsZxVbCCEETgyHsrIyfHx8bNO+vr6UlpYCoNVqWbBgAXFxcUyZMoXRo0cTGhpqW3fz5s3cdNNNuLq6Ulpaiq+vb4f7EUII4RxO7XNoy2Kx2N4bjUZWrVrFpk2b0Ol0JCYmUlBQQEREBAAbNmwgNTX1oj8jPz/fbtpkMjnM6836Wn2g79VJ6tPz9bU6Oas+TgsHvV5PWVmZbbqkpAQ/Pz8ADAYDwcHBtjOC2NhY8vLyiIiIoKamhtOnTxMUFNThfoqLi9Hr9R1+ZmRkpN10fn6+w7zerK/VB/penaQ+PV9fq9Pl1icnJ6fD+U5rVpowYQKZmZkA7Nu3D71ej06nAyAwMBCDwYDJZAIgLy+PIUOGAFBQUMDQoUNt+wkKCsJoNHLixAkaGxv58ssvmTBhgrOKLYQQAieeOYwdO5aoqCgSEhJQKBSkpKSwceNGPD09iY+PJykpiTlz5qBSqRgzZgyxsbEADn0MAM888wyPPfYYANOnT7frnxBCCNH1nNrn8Pjjj9tNt/QpACQkJJCQkOCwzdSpU5k6dardvGuvvdZ2GawQQgjnkzukhRBCOJBwEEII4UDCQQghhAMJByGEEA4kHIQQQjiQcBBCCOFAwkEIIYQDCQchhBAOJByEEEI4kHAQQgjhQMJBCCGEAwkHIYQQDiQchBBCOJBwEEII4UDCQQghhAMJByGEEA4kHIQQQjiQcBBCCOHAqY8JXbZsGXv27EGhUJCcnExMTIxt2dq1a8nIyECpVBIdHc3ixYsBSEtLIyMjA7VaTUpKCjExMcyePZuamhrc3d0BWLRoEdHR0c4suhBCXNWcFg7Z2dkUFhaSnp6OwWAgOTnZ9hxoo9FIWloamzdvRq1WM2/ePHJzc/Hw8OCTTz5hw4YNHDhwgC1bttgC5U9/+hMjRoxwVnGFEEK04bRwyMrKIi4uDoCwsDAqKysxGo3odDo0Gg0ajcZ2NlBbW4u3tzefffYZ06ZNQ61WExUVRVRUlLOKJ4QQ4gKcFg5lZWV2B3dfX19KS0vR6XRotVoWLFhAXFwcWq2WGTNmEBoaSlFRESqViqSkJBobG3nqqaeIiIgAYOXKlZSXlxMWFkZycjKurq7OKroQQlz1nNrn0JbFYrG9NxqNrFq1ik2bNqHT6UhMTKSgoACLxUJTUxP/+Mc/yMnJYfHixWzYsIE5c+YQHh7O4MGDSUlJYe3atSQlJTl8Rn5+vt20yWRymNeb9bX6QN+rk9Sn5+trdXJWfZwWDnq9nrKyMtt0SUkJfn5+ABgMBoKDg/H19QUgNjaWvLw8BgwYwNChQ1EoFMTGxlJUVARAfHy8bT+33HILn376aYefGRkZaTedn5/vMK8362v1gb5XJ6lPz9fX6nS59cnJyelwvtMuZZ0wYQKZmZkA7Nu3D71ej06nAyAwMBCDwYDJZAIgLy+PIUOGMGnSJL755hvAGiADBw7EYrEwd+5cqqqqANixYwfDhw93VrGFEELgxDOHsWPHEhUVRUJCAgqFgpSUFDZu3Iinpyfx8fEkJSUxZ84cVCoVY8aMITY2FoCvv/6au+++G4AlS5agUCi46667mDt3Lm5ubvj7+/Pggw86q9hCCCFwcp/D448/bjfd0rkMkJCQQEJCgsM2CxcuZOHChXbzpk+fzvTp051TSCGEEA7kDmkhhBAOJByEEEI4kHAQQgjhQMJBCCGEAwkHIYQQDiQchBBCOJBwEEII4UDCQQghhAMJByGEEA4kHIQQQjiQcBBCCOFAwkEIIYQDCQchhBAOJByEEEI4kHAQQgjhQMJBCCGEAwkHIYQQDpz6JLhly5axZ88eFAoFycnJxMTE2JatXbuWjIwMlEol0dHRLF68GIC0tDQyMjJQq9WkpKQQExNDQUEBzzzzDADh4eGkpqY6s9jCySwWCxV1FZw1naWsroyahhrc1G4oFIruLpoQopnTwiE7O5vCwkLS09MxGAwkJyeTnp4OgNFoJC0tjc2bN6NWq5k3bx65ubl4eHjwySefsGHDBg4cOMCWLVuIiYlh6dKltnB57LHH2Lp1K5MnT3ZW0cVlaDI3ccZ0huJzxRTXtHmda/23pKaEenN960Z7QK1U4+XihbfWG28Xb7y0Xni7eOOttb4/3zJPF0/USqf+xhHiquS0/6qysrKIi4sDICwsjMrKSoxGIzqdDo1Gg0ajoaamBnd3d2pra/H29uazzz5j2rRpqNVqoqKiiIqKor6+nqKiIttZx5QpU8jKypJw6AYNTQ2U1JbYHejbB0BZbRlNlia77TRKDf7u/vh7+BPjF2N77+vqy5HjR3D3daeyrpKq+ioq6yqprK+ktKYUQ4WByrpKjA3GC5ZLp9FZQ8TFyy44Wua1DZWWaS8XLzlbEeICnBYOZWVlREVF2aZ9fX0pLS1Fp9Oh1WpZsGABcXFxaLVaZsyYQWhoKEVFRahUKpKSkmhsbOSpp57Cx8cHLy8v23769+9PaWmps4p91appqKG4xvqr3uHA3/z+rOmsw3buanf8Pfzxd/dn/MDxtvctAeDv7k8/bb/zHoTzTflERkZesGyN5kaq66ttwVFVV0VlfaU1UOqq7EKlsq6SkpoS27JGS+N596tRahzORuzOUjo6i3HxwtPFE5VSdXF/YCF6mSt2Pm6xWGzvjUYjq1atYtOmTeh0OhITEykoKMBisdDU1MQ//vEPcnJyWLx4MX/729/Ou5/28vPz7aZNJpPDvN7sUupjsVg413SOs/VnOVN/pvXfhtbps/VnOdd0zmFbT7UnvhpffF18Ges5Ft/+1vf9XfrT36U/vi6+uKvcHT/UDBjBYrRwuvl/XVUnF1wY0Pw/ALTNL8+O615nrsPYaLS+moyt7xuNnGs61zpda+R74/e2aZPZdMFyeKg88FB7oFPp0KmtLw+1B2qzGrcTbqgVajRKDRqFBrVSbf23ed7FLFMr1CgV3XfdSF/7bwj6Xp2cVR+nhYNer6esrMw2XVJSgp+fHwAGg4Hg4GB8fX0BiI2NJS8vjwEDBjB06FAUCgWxsbEUFRXh6+tLRUWFbT/FxcXo9foOP7P9L9D8/B/+VdqbtK+P2WLmrOmsXVt+21/6LdO1jbV2+1GgYIDbAPzd/RnuNbz1136bX/16dz2uatcrXqeeoqGpwXpGUt/u7KSjs5f6Kk7WnaTyXCXGeiONlkYsnP9HzMVSK62B4aJywUXpgovKxW5ao9LY/euial3Htl67ddvuw267Nss0Kg1nj54lMjjSfn2lplf38/TU/89dqsutT05OTofznfYNT5gwgVdeeYWEhAT27duHXq9Hp9MBEBgYiMFgwGQy4erqSl5eHpMnT2bYsGGsX7+eW2+9FYPBwMCBA9FoNAwdOpRdu3YRGxvL5s2bmT17trOK7TQWi4VGSyP1TfXUNdVR31Rv976uqY66pjoazA229+3XOXLqCI0lja1hUFtCo9m+2UStUKN31+Pv4U+4bziTgiY5HPgHuA9Ao9R001+id9CoNPR3609/t/4XtV1+fj4RERE0WZqob6qnwdxg/R7N9bbvs8Hc0Dq/eVlDU4NtnZZl7depb6qn0dxov7/mbeua6jA2GFv31WadBnMDDU0NF2xiu6DvHGcpFcrLC6R267ioXFAr1XbTtm1b1m/ZR7swa5lWK9XSh9SFnBYOY8eOJSoqioSEBBQKBSkpKWzcuBFPT0/i4+NJSkpizpw5qFQqxowZQ2xsLABff/01d999NwBLliwBIDk5mSVLlmA2mxk9ejQ33nhjl5XT1Ggi/2y+3UG67UHZNm2uP+86nVle11R32b8mXZQuDNQNxN/dn7H+Y+0P+s3/+rr6dmszhACFQoFaoe5xv66bzE3WwGkJjeZA6iiYWv4/XXi8EL8Avw4DqX3wdbSfqsaqDtdpG4BdRYHCLnQcgqg5SBpqG/A56dMaSG3C7XxnYg6B13xmmSDUJwAACBtJREFU1VGYtV+3twaWwnKhRvxeJCcnh3HjxtnN68zp1tLtS1l/YH2nPkOr0tr+j6BVae3+tXuvtF+uVWnRqDS29y3zNUr7ee33236dQwcOMXLkyEv+G/VEcorfszm7PhaLxTFkms902p5tnTeQ2oRZS/Cd78ypZbrSWIlaq27db0tQtVmnq5sFzxsg7c6GOgq39k197c/UGs80cvt1t19y+To6dsIV7JDuqRaOXciUwVMcDvztD+gapabbfwF09+cL0dUUCoXtQHel/FDgtTQBdxRGHTb7tTv7Ol/T4YW2rWuso9pc3WHTYcu67S8Rb6FSqJg6Zirumg4uDrkMV304eLp4cuOgrmumEkL0bgqFAo1C0+P65c7XLHjy+5NdHgwg4SCEEL2CSqlCpVThiv1VhDWaGqd8nvRcCiGEcCDhIIQQwoGEgxBCCAcSDkIIIRxIOAghhHAg4SCEEMKBhIMQQggHfWr4DCGEEBevo+Ez+kw4CCGE6DrSrCSEEMKBhIMQQggHvXZspWXLlrFnzx4UCgXJycnExMTYlm3bto0VK1agUqmYNGkSCxYs+MFteoKLrdOOHTt46KGHGD58OAAjRozg6aef7q7iO7hQferq6liyZAmHDh1i48aNndqmJ7jYOvXm72j79u2sWLECpVJJaGgoS5cuRalU9ujv6GLrs3Pnzh79/cCF6/Tee+/xr3/9C6VSSUREBCkpKSgUiq75jiy90I4dOyz33XefxWKxWA4fPmy566677JZPmzbNcvLkSUtTU5PlnnvusRw6dOgHt+lul1Kn7du3Wx588MHuKO4P+qH6PPvss5Y333zTcvvtt3d6m+52KXXqzd9RfHy85dSpUxaLxWJ58MEHLV999VWP/o4upT49+fuxWC5cp5qaGsucOXMs9fX1FovFYpk9e7YlJyeny76jXtmslJX1/+3dT0jTfxzH8eecSR1KNt1WUKOQWAc7dKjLzBFl9McOERQy0kNZEiYOIoICqQ512K06FBkE2aFgmHgpiCCQDhMlskLMCCxKnfhvw8qBv0M0+rG12Td13+/v93qcHJ99Pvu+vm/1zb7f7ft9wa5duwAoKytjcnKSeDwOwNDQEMXFxaxZs4aCggICgQAvXrzIOscMjGQys1z7OxQKpcbnOyffjGQys1x5IpEIq1evBsDpdDI+Pm7qGhnJY3bZMq1YsYK7d++ybNkyZmZmiMfjuFyuBauRJZtDLBbD4XCkHjudTkZHRwEYHR3F6XSmjWWbYwZGMgG8e/eOhoYGampq6OrqWtqNziLX/v55P/E/mZNvRjKB9Ws0MjJCV1cXgUDA1DUykgfMWx+Y39/ErVu3qKqqYs+ePaxbt27BamTZcw6/mjPwaVwjc5bSfLZv/fr1NDY2snfvXoaGhqitreXJkycUFS3dXbXmSzWyZo3GxsZoaGigpaXlX/9wss0xi/nksVJ9IHOmEydOUFtbS319fcbvKxitkSXfObjdbmKxWOrxyMgILpcr49jw8DButzvrHDMwksnj8bBv3z5sNhter5fS0lKGh4eXfNszMbK/rVyj37FyjeLxOPX19TQ3N1NRUTGvOflkJI+Z6wPZM01MTBCNRgFYvnw5lZWV9PT0LFiNLNkc/H4/jx8/BuD169e43e7UW8a1a9cSj8f5+PEjyWSSZ8+e4ff7s84xAyOZOjo6aG1tBX4cehobG8Pj8eQtw6+M7G8r1+h3rFyjq1evUldXR2Vl5bzn5JORPGauD2TPlEwmOXfuHIlEAoBXr16xYcOGBauRZb8hHQ6H6e7uxmaz0dLSwps3b1i5ciVVVVVEo1HC4TAAu3fv5tixYxnnbNq0KZ8R0vxppng8zpkzZ5iammJ2dpbGxsbUcVQzyJanqamJL1++MDAwQHl5OYcPH+bAgQOWrlGmTDt27LBkjSoqKti6dStbtmxJPbe6upojR46YukZ/mmf//v2mrg9k/52LRCK0tbVRWFiIz+fj4sWL2Gy2BamRZZuDiIgsHkseVhIRkcWl5iAiImnUHEREJI2ag4iIpFFzEBGRNGoOIiKSRs1BRETS/CeurSSymKanp2lqauLr168EAgEePHhAc3Mz9+7do6CggI0bN3L58mUikQjRaJTx8XEGBgYIhUJ0dnYyODhIOBympKSEs2fP4vV66e3tpaamhv7+fl6+fEkwGCQYDNLR0ZG2rkg+qDmI5NDe3k5ZWRkXLlygra0NgJmZGW7fvs2qVasIBoP09/cD8OHDB+7fv8/Dhw+5efMm7e3tRCIROjs7qaur4+3bt9y4cYPJyUmqq6t5+vQp37594/Tp0wSDwYzr+ny+fMaX/yk1B5EcBgcH2bZtGwA7d+6ktbWV4uJiTp06lRqfmJgAoLy8HJvNhsvlwufzYbfbKS0tpaenBwCv14vD4aCoqAin04nH4yGRSDA9PQ3w23VFlpqag0gOc3NzFBT8OD1ns9n4/v07ly5d4tGjR7hcLk6ePJl6bmFhYcaff16lxm63ZxwHsq4rstR0QlokB6/XS19fHwDPnz8nkUhgt9txuVx8/vyZvr4+Zmdn//p1FmtdESPUHERyOHjwIN3d3Rw9epRYLEZJSQl+v59Dhw5x/fp1jh8/zpUrV0gmk3/1Og6HI+O6ahCSD7oqq0gOnz594v3792zfvp3e3l6uXbvGnTt38r1ZIotKzUEkh6mpKUKhUOqmKufPn2fz5s153iqRxaXmICIiaXTOQURE0qg5iIhIGjUHERFJo+YgIiJp1BxERCSNmoOIiKT5B6ftZpyIUiU1AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"LGQAsMfRxesB"},"source":["The possible optimal gamma is 0.1. Because the two parameters may be interactive, we need to use `GridSearchCV` to find the optimal `C` and `gamma`."]},{"cell_type":"code","metadata":{"id":"doXaseehoUQo"},"source":["from sklearn.model_selection import GridSearchCV"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mRnAE2SSx3EZ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1631982811427,"user_tz":-480,"elapsed":2954548,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"1117ffe8-25c9-4dda-800e-29080ebfd0ba"},"source":["param_grid={\n","  'C':np.arange(0.5, 2, 0.5),\n","  'gamma':np.arange(0.01, 0.08, 0.02), \n","}\n"," \n","grid = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=5, scoring='accuracy')\n","grid.fit(X_train_standard,y_train)\n","print(\"best_params:\", grid.best_params_)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["best_params: {'C': 1.5, 'gamma': 0.06999999999999999}\n"]}]},{"cell_type":"markdown","metadata":{"id":"WmXXnMityzmp"},"source":["### Evaluation"]},{"cell_type":"code","metadata":{"id":"NZaU70BdYHkU","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"ok","timestamp":1632118862881,"user_tz":-480,"elapsed":97244,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"1913dbd4-7bf7-404c-a893-319ac028cd95"},"source":["svc1 = svm.SVC(kernel='rbf', C=1.5, gamma=0.07)\n","svc1.fit(X_train_standard,y_train)\n","y1_predict = svc1.predict(X_test_standard)\n","mccs.append(evaluation(y_test,y1_predict))"],"execution_count":32,"outputs":[{"output_type":"stream","name":"stdout","text":["TN,FP,FN,TP: 3721 265 234 302\n","mcc value: 0.485064053351926\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Q80fgB1Nc1YY","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"ok","timestamp":1632119399857,"user_tz":-480,"elapsed":319845,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"d69fb6e0-c9b1-4d0f-e5ea-bd8cb3946234"},"source":["svc2 = svm.SVC(kernel='rbf', C=1.5, gamma=0.07)\n","svc2.fit(X_train_all_feature_standard,y_train_all_feature)\n","y2_predict = svc2.predict(X_test_all_feature_standard)\n","mccs_a.append(evaluation(y_test,y2_predict))"],"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["TN,FP,FN,TP: 3867 119 373 163\n","mcc value: 0.36659807070017186\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"tQsTB2FjXWn2","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"ok","timestamp":1632119631124,"user_tz":-480,"elapsed":192775,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"b6911443-0ada-468b-c520-e51678b36bca"},"source":["svc3 = svm.SVC(kernel='rbf', C=1.5, gamma=0.07)\n","svc3.fit(X_g_train_standard,y_g_train)\n","y3_predict = svc3.predict(X_g_test_standard)\n","mccs_g.append(evaluation(y_g_test,y3_predict))"],"execution_count":35,"outputs":[{"output_type":"stream","name":"stdout","text":["TN,FP,FN,TP: 3781 195 260 286\n","mcc value: 0.5017431788271114\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"5QfdvZzqFVuW"},"source":["## 6.3 Decision Tree"]},{"cell_type":"markdown","metadata":{"id":"MAkpeEAD0Afc"},"source":["Tune the parameters. First we look at `max_depth`."]},{"cell_type":"code","metadata":{"id":"VIZ38Uy9d8m8","executionInfo":{"status":"ok","timestamp":1632119683665,"user_tz":-480,"elapsed":323,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["from sklearn.tree import DecisionTreeClassifier "],"execution_count":36,"outputs":[]},{"cell_type":"code","metadata":{"id":"NAjIYLbc00Wb","colab":{"base_uri":"https://localhost:8080/","height":276},"executionInfo":{"status":"ok","timestamp":1631983063408,"user_tz":-480,"elapsed":7250,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"17e92714-f9ae-4745-d3ff-d0056ad29076"},"source":["ds = range(2,21,2)\n","dt = []\n","for depth in ds:\n","  DT = DecisionTreeClassifier(max_depth = depth)\n","  DT.fit(X_train, y_train)\n","  train_accuracy = DT.score(X_train, y_train)\n","  test_accuracy = DT.score(X_test, y_test)\n","  cv_accuracy = np.mean(cross_val_score(DT, X_train, y_train, cv=5, scoring='accuracy'))\n","  dt.append([depth, train_accuracy, test_accuracy, cv_accuracy])\n","dt = pd.DataFrame(data=dt,columns=['depth', 'train accuracy', 'test accuracy', 'cv accuracy'])\n","results = pd.melt(dt,id_vars=['depth'],var_name='type',value_name='accuracy')\n","\n","sns.lineplot(x='depth', y='accuracy', hue='type', data=results)\n","plt.xticks(ds)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"5eb6dtBC0LM7"},"source":["Then we look at `min_samples_split` with `max_depth` is 10."]},{"cell_type":"code","metadata":{"id":"6Xvn9XRb0Pqm","colab":{"base_uri":"https://localhost:8080/","height":276},"executionInfo":{"status":"ok","timestamp":1631983079131,"user_tz":-480,"elapsed":15732,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"6cf01195-6db1-4d59-d40a-b2dee03a3c49"},"source":["sss = range(2,20,1)\n","dt = []\n","for ss in sss:\n","  DT = DecisionTreeClassifier(min_samples_split=ss, max_depth=14)\n","  DT.fit(X_train, y_train)\n","  train_accuracy = DT.score(X_train, y_train)\n","  test_accuracy = DT.score(X_test, y_test)\n","  cv_accuracy = np.mean(cross_val_score(DT, X_train, y_train, cv=5, scoring='accuracy'))\n","  dt.append([ss, train_accuracy, test_accuracy, cv_accuracy])\n","dt = pd.DataFrame(data=dt,columns=['samples split', 'train accuracy', 'test accuracy', 'cv accuracy'])\n","results = pd.melt(dt,id_vars=['samples split'],var_name='type',value_name='accuracy')\n","\n","sns.lineplot(x='samples split', y='accuracy', hue='type', data=results)\n","plt.xticks(sss)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEDCAYAAAA4FgP0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXxTVfr48U/SJm3TpEu60pW2Aq1l32RzQQuOOOi4QWFYHBj96sjo4OCoRYZBBxRFHUUHlZ87oiiiMjMqCCIu7CB72VpaukDbdE/bdMv9/ZE2bWmBboFCnjevvJJ7b87Tkzac595zzz1XpSiKghBCCKelvtQVEEIIcWlJIhBCCCcniUAIIZycJAIhhHBykgiEEMLJSSIQQggn53qpK9BWu3fvvtRVEEKIy9KgQYNa3qBcZnbt2tWh8ocPH+5wHTojhsSROF0hTleqi8RxbJzztZ3SNSSEEE5OEoEQQjg5SQRCCOHkJBEIIYSTk0QghBBOThKBEEI4OUkEQgjh5C67C8raq6i8ijEv/4jRDRJOqRh5lT8DI3xx17hc6qoJIcQl5TSJwMtdw4yRUfxn90ne2JzK65tScHNVMzTKyIgYf0Ze5Ud8iDcuatWlrqoQQlxUTpMI1GoVD94Qww1BVYRFXcWOkwX8fMLElhP5LP72CADeHhqGR/sxsoc/I2P8iPL3RKWSxCCEuLI5TSJozOCu4aa4IG6KCwIgt9TC1pR8fj5uYktKPt8eOgNAiLc7I67yZ9RV/oyI8SPQy/1SVlsIIRzCKRPB2QIN7tzeP5Tb+4eiKArp+eW2o4UUExuSc1i9OxOAHoF6Rl7lT4RbBf5hlQQY3C5xzYUQouMkEZxFpVLR3d+T7v6eTBkWidWqcPh0CT+fMPHLCROf7DyFpdrK05tyCPXxoF+4N33DfOgX5kOfMG/0bvIrFUJcXqTVugC1WkXvUG96h3rzwPUxVNbUsvbnfRS7+LA3o4h9mUV8fcDWlaRSwVUBevqF+9AvzJt+4T7EBnuhdZVRukKIrksSQRu5ubrQO8iDuLho+7qCsir2ZRaxL6OI/ZnFbDqSa+9O0rqoiQvxon9Y3ZFDuA/R/p6oZXSSEKKLkETQCYyeWkb3CmR0r0AAFEUhq6iCfRnF9gTx2e5M3t+aDoDBzZU+Yd6E62r5jTqXwZG+GNw1l/IjCCGcmCQCB1CpVIT56gjz1XFr324A1FoVUvLMtu6kuiOH1anFrDqwE7UK4kO8GRpl5JooI0O6G/H11F7iTyGEcBaSCC4SF7WKnkEGegYZmDA4HIBf9x+iXBfE9tR8tp8s4MNt6bz980kAYoMNdYnBj6FRRhmhJIRwGEkEl5C7Rs2Aq/wZeZU/AJbqWvZnFrM9NZ8daQV8tiuTD+q6k6IDPLmmUWII8fG4lFUXQlxBJBF0Ie4aF4ZGGRkaZQSgutbKwaxitp8sYMfJAv677zQf78gAINzowdDuflwTbcSvtppYRZGroIUQ7SKJoAvTuKgZEOHLgAhfHrg+hlqrQvLpkrrEkM/3R3L4fI9tdJLvujN1w1Z96B/hQ/8wHznPIIRoFUkElxGXRtc0zBwVhdWqcCLPzH+2Hianxp29GUVsPpaHotjeH+mno3+j5HB1Ny+ZbVUI0YwkgsuYuu4E9LheXsTFxQFgrqxhf2YR+zKK2ZtRyPbUAr7amw2AxkVFXDevJskhyk+uaRDC2UkiuMLo3VwZEePPiBh/+7ozxRb2ZhTVPQr5fHfDSWgvd1d7l1KgupzuMbV4aOWoQQhnIonACQR7u/Mb72B+0zsYsF3TcCLXzL6MIn6tu65h2eYUaq0KCzevZ1i0Hzf0CmB0r0C6+3te4toLIRxNEoETclGr6BVsoFewgQlDbNc0lFfVsObHfaRUuLP5aB4L/nOYBf85THc/HTf0CuSGXgEMi/aTcwxCXIEcmggWLVrEvn37UKlUJCUl0bdvX/u2DRs2sGzZMrRaLbfeeitTpkwB4NixY/zpT3/i3nvvta8TjqfTujIoVMeUuDgYD+n5ZfxwNI8fjuby8Y5TvLclDXeNmuHRftxQN51GhJ/uUldbCNEJHJYIduzYQXp6OqtWrSIlJYWkpCRWrVoFgNVq5ZlnnuGLL77Ax8eH++67j4SEBLy8vHjmmWcYPny4o6olWinSz5PpIzyZPqI7lupatqXm2xPDpqOHmM8hov097UcLQ6OMcrQgxGXKYYlg69atJCQkABATE0NxcTFmsxm9Xk9hYSFeXl4YjbYLp4YNG8aWLVu47bbbWL58OcuXL3dUtUQ7uGtc6hr8QCCek6Yyfjiayw9H8/hoezrv/HISD40LI2Js5xYClCqia2pxc5XEIMTlwGGJwGQyER8fb182Go3k5eWh1+sxGo2UlZWRlpZGaGgo27dvZ+jQobi6uuLqKqcturoof0+i/KP4w8goKqrqjxZy2XQ0j41HcgFQ/yeTMF8d0QGeRPvriQrwJMbfk+gAPUFebnIVtBBdyEVrdZX6q5ywzc753HPPkZSUhMFgICwsrE2xkpOT210Pi8XSofKdFeNKihMMJPZ0YWKPILJKqjl02kxOBWSVVHMqr5itKSYqaxr+/u6uKkK9NIR5aQjz1hDqpa171qDTNNzE51J/LmeI05XqInEuXpyzOSwRBAYGYjKZ7Mu5ubkEBATYl4cOHcrKlSsBePHFFwkNDW117PqLp9ojOTm5Q+U7K8aVGudqIOysOFarwpkSCydNZaTmmUk1lZGaV0aqycyP6UU02kcg0OBmO4oI0ONLFb8dFkPPIAMuHbjorSv9frpanK5UF4nj2Di7d+8+5zaHJYKRI0eydOlSEhMTOXToEIGBgej1evv2P/7xjyxevBgPDw82bdrEH/7wB0dVRVxiarWKEB8PQnw87DOt1rNU13KqoLxpgsgz8/WB0xSVV/P69p8wuLsyMMKXId19GRRppH+4j1z0JkQnclgiGDhwIPHx8SQmJqJSqZg/fz5r1qzBYDAwZswYJkyYwIwZM1CpVNx///0YjUYOHjzI4sWLycrKwtXVlXXr1rF06VJ8fHwcVU1xiblrXOz3aWhMURR+2HmAQlcju9IL2ZVWwJL1eQC41s25NDjSl8HdjQzu7ou/Xu7XIER7OfQcwZw5c5osx8bG2l+PHTuWsWPHNtneu3dvPvzwQ0dWSVwmVCoVwQYNo+PCuHOg7RxSUXkVe04VsivN9vhgWzr/r+5GPlH+ngyKbDhqiAnwlBPSQrSSDNERlw0fnZYbY4O4MTYIgMqaWg5mlbA7vYCdaYVsTM5h9W7btNxGT629OykACzE9rGhd1ecLL4TTkkQgLlturi4MivRlUKQv919n605KNZWxK63AdtSQXsiG5BwAkjacYUC4r/3GPwMifNBp5esvBEgiEFcQlUpFTICemAA9E4dEAGAyV/LlzwfIqvZgZ1oBS78/jlVpOM9wTZSRId1tD2+d5hJ/AiEuDUkE4ormr3djZKSnfchdiaWa3emF7DxZwM60At79JY03f0xFpYJeQQaG1iWGoVFGgrzcL3Hthbg4JBEIp+LlrmF03aR5YBu+ui+jiB0nC9iRVsDqRvdqiPTT2ZPC0O7GJhdFCnElkUQgnJq7xoVrov24JtoPgJpaK4eyS9iZVsD2kwVNTkB7atT06lZIj0ADPYL09AyyPQd7ucsIJXFZk0QgRCOuLmrbHdvCffjjtdH2+0LvTCtg6+FT5FWp+S45h1W7MuxlDO6u9Ai0JYar6p57BhlkTiVx2ZBEIMR51N8XumeQgYFe5fZzDfnmSo7lmDmeW8rxHDPHckpZfziHT3ZeOEFIF5PoaiQRCNEOfno3huvdGB7j12R9axKEr7sLg6LK6F935NE3zAdvDxmxJC4dSQRCdKILJYhjOaX8dCidk6YyNiTn2rdHB3jSP9zH/ogN9pIL4MRFI4lAiIugcYIY6ltBXFwcxRXVHMgsZm9GIXszivnxmIk1e7IA0LqqiQ/xol+YDwMibMkhwqiTcw7CISQRCHGJeHtoGNXDn1E9bDOyKopCdrGFvaeK2JdZxN5TRazamcF7W9IA8NVpbCeyw2yJQWWuppdVQd2BKbqFAEkEQnQZKpWKUB8PQn08uLVvN8A2nPVYjpm9GUXsyyhib0YRm48dt9/Dwe2rLKL8PRvuBFf/OkAv5x1Eq0kiEKILc3VRc3WIF1eHeDH5Gtu0GebKGg5lFfPzgRNYXA2k5pWRfLqUdYdyqLU2jEjy89TaE0R0gGddktATYdTJ+QfRhCQCIS4zejdXron2w6syt8ndqqpqrGQUlttv7nOy7kY/G4/ksGpXlf19LmoVEUYdUf6eGFQWIjKO4q5xwc1VjbvGpe6hxt210etG293qlt1dXdC4qOS8xRVAEoEQVwitq9o+6R4ENdlWXFHdcKvQvDJOmspIyTOTbjJjOVJCey9tUKtsV2e7uUD3gAIijToi/DyJMOqI9NMRadQRYJAL67o6SQRCOAFvD419aGpjycnJxMbGUlVrxVJtpbK6Fku1FUtNLZb619V1r2tsrysbr6+xvc44Y6LU6sLOtELW7sumUQ8V7ho1EUYdEUZPIv10ttd1SSLU1wM3V7nt6KUmiUAIJ6dSqXBzdbE1yO08wdz4pupVNVYyC8s5VWB7pOfbHhkF5fx8Ig9LtbXRz4YQb4+6RKHDw2pmQGUW4UYd4b46/PVaOZq4CCQRCCE6ldZVTXSAnugAfbNtiqKQV1rZkCAKbAkiPd92LsNkruK9PYX293toXAjz9SDM18OeHMKNHoT52l7LPSQ6hyQCIcRFo1KpCPRyJ9DLncHdjc2279l/CM/ACDILbQkio7CCjIJyMgsr2JVeSKmlpsn7De6uhPvqGiUK23NlQSWB5kp8dVq5zqIVJBEIIboMD42aXsEGegUbWtxeXF5NRl2SyCyssL9ONZXx4/Gm3U78JwsXtQo/Ty0BBjfbQ++Gf92zfZ3BDX+9G17urk7bDSWJQAhx2fDWafDWedM71LvZNkVRMJmryCgsZ+ehE7h5+WMyV5FXWkmeuZK80kqOnC7FZK6kxtp8mJTWVd0kQfjr3XCpLKVvWQahPh5083YnxMcDd82Vd3JbEoEQ4oqgUqnsjbhHmZ64uKgW32e1KhRXVNuTQ15pJaZGr/PMlWQUlPPrqUJM5irYV9ikvJ+nlpBGiSHUx8O27ONOqI8HAXq3y647ShKBEMKpqNUqfD21+Hpq6RnUchdUvX0HD+PbrTtZRRWcLq4gu6iCrCIL2UUVpOWX8csJE2VVtU3KaFxUBHu70827Pkm4o7aUkOeSR7hRR4iPe5cbMiuJQAghzkHroiLCz3bdQ0sURaHEUkN2kS1JZBdbGl4XVbDjZAFnSiy2qT+2mgDbkNlgL3fbCW5fHWF1J7nD6kZEdfP2wOUiH1FIIhBCiHZSqVR4e2jw9tAQ182rxffU1Fr5Zc8h3PxCmpzkziyoYGtqPmf2ZjW5sttVrSLEx8M2TNbHlhzCjTrCfHVoWzi30RkkEQghhAO5uqgJ1LsSF+3HsGi/ZturaqxkF9WPgKqwDZ2tGza78UguJnOl/b2T+vrwbLwD6tj5IYUQQrSW1lVNd39Puvt7tri9oqqWzMJyMosq0JXntviejnLoXLSLFi1i4sSJJCYmsn///ibbNmzYwF133cWkSZNYsWJFq8oIIYSz8dC60CPIwOhegXi5O+Yks8OOCHbs2EF6ejqrVq0iJSWFpKQkVq1aBYDVauWZZ57hiy++wMfHh/vuu4+EhAROnTp1zjJCCCEcw2GJYOvWrSQkJAAQExNDcXExZrMZvV5PYWEhXl5eGI22S8yHDRvGli1byMjIOGcZIYQQjuGwriGTyYSvr6992Wg0kpeXZ39dVlZGWloa1dXVbN++HZPJdN4yQgghHOOinSxWGo2PUqlUPPfccyQlJWEwGAgLC7tgmcaSk5PbXQ+LxdKh8p0VQ+JInK4QpyvVReJcvDhnc1giCAwMxGQy2Zdzc3MJCAiwLw8dOpSVK1cC8OKLLxIaGkplZeV5y9RrfHu+tmo8b/qljCFxJE5XiNOV6iJxHBtn9+7d59zmsK6hkSNHsm7dOgAOHTpEYGBgk77+P/7xj+Tn51NeXs6mTZsYPnz4BcsIIYTofA47Ihg4cCDx8fEkJiaiUqmYP38+a9aswWAwMGbMGCZMmMCMGTNQqVTcf//9GI1GjEZjszJCCCEcy6HnCObMmdNkOTY21v567NixjB079oJlhBBCOJZDLygTQgjR9UkiEEIIJyeJQAghnJwkAiGEcHKSCIQQwslJIhBCCCcniUAIIZycJAIhhFOpn71ANJBEIIRwGpmZmfzvf/+71NXocuRWlUIIp/H000+zf/9+evXqxZ49e/D09GT37t28++679OrVizNnznD69Gny8vJ47LHHCAgIYP369bzzzju4urrSu3dvnnjiiUv9MTqdHBEIIZzGzJkzGTp0KDNmzOD7778HYOPGjfz2t78FICcnh3feeYclS5bw0ksvUVFRwbJly/jggw9YsWIFp0+fPu8snpcrSQRCCKdz++238/XXXwO22+qOHj0agOHDhwPQq1cvcnJyyMjIIDs7m5kzZzJ16lTS09PJzs6+ZPV2FOkaEkI4ndjYWEwmE/v376dHjx64ubkBtvupN1bfHfT2229fimpeNHJEIIRwGmq1mpqaGgBuueUWnn76acaPH2/fXt/tc+TIEUJCQggNDSUlJYX8/HwAXn31VXJyci5+xR1MjgiEEE4jJiaGw4cPs2jRImbMmME777zDsGHD7Nv1ej0PPPAAWVlZJCUl4ebmRlJSEvfddx9arZarr76awMDAS/gJHEMSgRDCaRiNRn744QcAPv/8cyZMmIBa3dAx0r9/f6ZMmWJfTk5OPue9U64kkgiEEE7nqaeeIiMjg9dff/1SV6VLaFUiWLx4Mb/97W+Jj493dH2EEMLh/vnPfzZb9+c///kS1KRraFUiuPrqq1m+fDlZWVnccMMN3HbbbYSHhzu6bkIIIS6CViWC8ePHM378eKqrq9m2bRuPPvooarWaxMREfve736FSqRxdTyGEEA7S6nMEe/fu5X//+x87duxgyJAh3HLLLWzZsoW//OUvvPLKK46soxBCCAdqVSK4+eabiY2N5fbbb+fxxx/H1dVWbNCgQfzf//2fQysohBDCsVp1QdmqVauYOXMmN954I66urmzduhVFUQB48803HVpBIYRoi7ZMM71w4UIyMjIcWJvLQ6sSwaJFi1i/fr19eefOnVfkDHxCiMtbW6eZnjt3rgx8oZVdQ9nZ2Tz//PP25YcffpipU6c6rFJCCNEe9dNMG41G/Pz8yMjIIDMzk/fee48nn3ySnJwcysvL+fOf/8zo0aOZOnUq8+bNY926dZSWlnLy5ElOnTpFUlIS119/vT1uTU0Njz/+eLPyhw8fZsGCBahUKgYMGMDjjz/e4rq5c+fy3HPP0bNnT1asWEFhYSFDhw7lnXfeoby8nMcff5wdO3awbt06rFYr119/PbNmzaKkpIQ5c+ZgNpsxGAzcd999JCQk8NVXXzWZQvu1117r0O+tVUcEKpWKH374geLiYgoLC/nmm2/s5wmEEKKrqJ9meuLEiQBUV1ezcuVKSktLGTVqFCtWrOCVV15h6dKlzcqeOXOG5cuXM3fuXFatWtVkW3FxcYvl//nPf7JgwQI++eQT8vPzycrKanHduRw7doy3336b3r17A7By5Uo+/fRT1qxZg9ls5u2332bUqFGsXLmS4cOHc+DAAcaMGdPiFNod0eoLyl5++WVeeOEF1Go1ffv25bnnnuvwDxdCXLk+353Jp7va1v9eXl6O7seic26fMDicuwaFtTpe3759AfDy8uLAgQOsWrUKtVpNUVHznzFw4EAAgoODKS0tbbLtXOVPnjxJbGwsgL3XpKV159KrVy+0Wi0A7u7uTJkyBVdXVwoLCykqKuLw4cM88sgjANx7770kJyejUql45ZVXGD9+PDt27LBv74hWJYKQkBBeeOEF+3J1dTULFixo8eo8IYToKjQaDQD//e9/KS4uZuXKlRQVFXH33Xc3e+/5ejnOVb7xPEX1WlrX+Fqr+tlPAXsSyMrK4r333uOLL77A09PTvpfv4uLSbGrsc02h3RGtSgSfffYZr776KoWFhWi1WqxWKzfccEOHf7gQ4sp116CwNu29g22St7i4uHb/zMbTTDdWWFhIWFgYarWa7777jqqqqjbFPVf5mJgY9u3bR79+/UhKSmLmzJktrtPpdOTl5dGzZ0/27NlDjx49msU3Go14enpy6NAhsrKyqK6upnfv3mzbto2+ffvau5ri4uLsU2g/+uij7f5dNdbq4aMbNmxgwIAB7NmzhxdffJEBAwZcsNyiRYuYOHEiiYmJ7N+/v8m2jz76iIkTJzJp0iQWLlwI2A4LH374YSZPnszMmTPJy8trx0cSQjir+mmmz76RzNixY/n++++ZPn06Hh4eBAcHt+kE67nK158EnjRpEt7e3sTExLS4buzYsTz99NPcf//9LU5jHRcXh6enJ4mJiXz99dckJiayYMECpk+fzq+//srUqVP54Ycf7HdQGzduHGfOnGkyhXaHKK0wefJkRVEUZeLEiUptba2iKIoyZcqU85bZvn27cv/99yuKoignTpxQJkyYYN9WWlqqjB49WqmurlYURVH+8Ic/KL/++qvy7rvvKs8//7yiKIqyc+dO5amnnmoWd9euXa2p8jkdPny4Q+U7K4bEkThdIU5XqovEaX2c1atXK6+88kqbyp6v7WxV11CfPn1YsWIFo0aNYvr06QQHB2OxWM5bZuvWrSQkJAC2LF1cXIzZbEav16PRaNBoNLYTQzodFRUVeHt7k5aWZs94gwcP5u9//3sH05wQQlxZHDGFdqsSwYwZM/Dx8UGr1XLNNddQWFjIiBEjzlvGZDI1mbbaaDSSl5eHXq/Hzc2Nhx56iISEBNzc3Lj11luJioqiZ8+ebN68mZtvvpkdO3ZckTeJFkKIjnDEIJ1WJYJHH32UFStWADBkyJB2/SClbkoKALPZzJtvvsm3336LXq9n+vTpHDlyhLvvvpujR48yadIkhg4ditFobDFWcnJyu+oAYLFYOlS+s2JIHInTFeJ0pbpInIsX52ytSgQBAQEkJibSp08f+3AsgL/97W/nLBMYGIjJZLIv5+bmEhAQAEBKSgrh4eH2hn7w4MEcPHiQ2NhYFixYAEBZWRkbN25sMXZHRhV0dFRCZ8WQOBKnK8TpSnWROI6Ns3v37nNua1UiuO6665qtu9A9CEaOHMnSpUtJTEzk0KFDBAYGotfrAQgNDSUlJQWLxYK7uzsHDx7k+uuvZ/Pmzfz666/85S9/Ye3atVx77bWtqZ4QQogOaPU8EW29+czAgQOJj48nMTERlUrF/PnzWbNmDQaDgTFjxjBz5kymTZuGi4sLAwYMYPDgwVgsFj766CMmTJiAt7c3L730Ups/kBBCiLZpVSI4duyY/XVNTQ379u2jR48e/O53vztvuTlz5jRZrr/sGiAxMZHExMQm293d3XnrrbdaUyUhhGjRunXriIiIaFOZnTt3Eh0djZ+fn4Nq1bW1KhE8/vjjTZZra2t5+OGHHVIhIYRor/ppqB988ME2lfv888+ZMWOGJILzqaioaLKcl5dHamqqQyokhBDt1Xga6jlz5pCUlERxcTG1tbU89dRTxMbG8tZbb/Hdd9+hVqsZPXo0ffr0YcOGDRw/fpylS5cSEhIC2Ho/XnrpJSoqKto19XT9FNc9e/bkf//7Hxs2bGjX1NNLlizhjjvu4KuvvgLotKmnG2tVIrj11ltRqVQoioJKpcJgMDBjxoxOq4QQQnSGmTNn2qevef/997n22mu55557OHHiBAsXLuTdd9/lnXfe4eeff8bFxYWPP/6YkSNHEhcXx7x58+xJAGxTT/fv35+HHnqIjIwMHnnkEUaPHm2fZjo2Npa//e1vTaaebrzuXI4dO8a6devQarXs2LGDlStXolaruemmm7j33nvtU09PmzaN9957j+3bt9unnr7qqqs6berpxlqVCL7//nsqKyvts9yVlpZiMBg6tSJCiCvM3o/h1xVtKhJRXgbbPM/9hgFToP+kVsX69ddfKSgoYO3atUBDz8bNN9/MH/7wB377299y2223nbO8l5cXJ06cIDEx8ZJOPQ0QFhbGK6+8wsMPP9xpU0831qpE8MEHH7BlyxbeeOMNAB577DFGjBjBtGnTOrUyQgjRWTQaDfPmzWs2QeaCBQtISUnhm2++YerUqXz22Wctlv/vf/9LaWlpu6eebqy2ttb+uiNTTx8/frzTpp5urFWJ4Ouvv2blypX25WXLljFp0iRJBEKIc+s/qdV77/VOdeI01P369bPPmnzixAl++ukn7r77bt5//31mzZrFrFmz2LVrF2azGZVK1aSxBtvU0EFBQe2eelqv19unnk5OTmbQoEHN4rdm6mk3NzfuuOMObrnlFt566y2SkpLa/fs55++tNW+qqamhpKTEvizTQwshuqLG01BPmTKFU6dOMXnyZJ566ikGDx6MwWCgsLCQu+++m2nTptGvXz98fHwYOnQoDz/8MMePH7fHGjt2LDt37mz31NMTJ060Tz3d0nQ5rZ16esyYMYBt6mmTydR5U0831prpS3/++Wdl7Nixyvjx45Vbb71VueWWW5StW7e2aQrUziLTUEscidN5cbpSXSTO+a1evVqZN29eu8t3eBrqkSNHsnbtWsrKylCr1bi4uMjJYiGEuEjqp57u7JPE9VqVCN5//322bt1qP1n8wAMPyMliIYS4SOqnnnbEzKPQynME33zzDf/+97/ty8uWLePrr792SIWEEEJcXHKyWAghnFyrb0wzceJE3NzcUBSF2tpaZs6c6ei6CSGEuAhadURgMBjo1auXfZytl5eX/XyBEEKIy1urEsE///lPJk+ejE6n49lnn+Waa65xyEUNQgghLr5WJQJ3d3eGDRuGRqOhd+/ezJ49234PYyGEEJe3ViUCDw8PNm7cSFhYGC+99BKfffYZp0+fdnTdhBCiTaqrq/nrX//KE088wfTp08nJyeGOO+4gOzsbsM3vc+eddzYpc+TIERCQvcwAACAASURBVCZNmsTUqVOZPn26fXK55cuX89hjjzFhwgS2bdtmX3f33Xfb12VmZjaJd+edd5KZmckTTzzBvHnz+POf/4zZbOaf//wnU6dO5Z577mH//v0A/PLLL9x1111MmDCB9957j82bN/PYY4/ZYz311FPnvG97Z2tVIliyZAkxMTH8/e9/R6vVcvToURYvXuzougkhRJt8+eWX+Pv789xzzzFhwgQ2btxIQkICmzZtAmDjxo2MHTu2SZn8/HzmzZvHhx9+yMCBA/nPf/5DWloa69atY/HixbzwwgtN1n366af2defj7e3N0qVLycvLIyEhgQ8//JBHH32U5cuXoygKCxYsYPny5Xz88cds3bqVIUOGsH//fiorK7FarezZs+ei3be9VaOG9Hq9/cbzs2bNcmiFhBBXhrUpa/ni+BdtKlNeXo4uXXfO7Xf0uIPbYs49dfShQ4cYPnw4YLuPCsDx48d57rnn+P3vf8/GjRv5xz/+0aSMn58fS5YswWKxkJuby/jx4zl8+DD9+vVDrVYTGRnJwoUL+frrr5uty8zMPGdd+vbtC4C/vz9bt25l/fr1VFVVodPpKCgowM3NzT4H0ZtvvgnADTfcwObNmwkICGDw4MH2mUodrVVHBEIIcTloaQrnHj16kJuby+nTpyktLSUqKqrJ9oULFzJt2jRWrFjBxIkTzxmnpXUqlarJcv3Mp2CbBhtsMzP4+fnx8ccf25OQWq1uFgvgd7/7Hd9++y3ff/99p9985nxadUQghBBtdVvMbefde29Jcgenoe7Tpw/btm2je/fubNq0iaNHj/LAAw9www038PLLL3PjjTc2K1NUVERERARVVVVs3ryZ/v37Ex8fz7///W/uuusuTCYT8+fP58knn+Tf//43NTU1FBUVMX/+fBYtWkR+fj6KomAymcjIyGgWv7CwkODgYAA2bNhAdXU1vr6+1NbWkpOTQ2BgIA888AAvvPACcXFx5OTkkJ+fz6OPPtru30NbSSIQQlwxxo0bx5YtW5g7dy5eXl72c5ljxowhMTHRfreyxqZMmcJDDz1EeHg4U6dO5emnn2bcuHHcfvvtJCUl4eHhwezZswkLC+P2229nypQpKIrC7Nmz8fb2ZsSIEdx1113Exsa2mMRuv/12Zs+ezb59+/j973/Pf//7Xz7//HPmz5/Pww8/DMAtt9yCl5cXYJvks6ysrNnRhkO1e07TS0SmoZY4Eqfz4nSlukgcRbFarcr06dOVtLS0Tq/P+dpOOUcghBBdQGZmJnfddRcjRowgMjLyov5s6RoSQoguICwsjDVr1lySny1HBEII4eQkEQghhJOTRCCEEE7OoecIFi1axL59+1CpVCQlJdmvtAP46KOPWLt2LWq1mt69ezN37lxycnJISkqiqqoKq9XKk08+Se/evR1ZRSGEcHoOSwQ7duwgPT2dVatWkZKSQlJSEqtWrQLAbDbz9ttvs379elxdXZkxYwZ79+5l3bp19vG+e/bs4eWXX+btt992VBWFEELgwK6hrVu3kpCQAEBMTAzFxcWYzWbAdum1RqOhvLycmpoaKioq8Pb2xtfX1z7zX0lJCb6+vo6qnhBCiDoOOyIwmUzEx8fbl41GI3l5eej1etzc3HjooYdISEjAzc2NW2+9laioKO69917uvvtuvvzyS8xmMx9//LGjqieEEKKOSlEUxRGB582bx/XXX28/Kpg0aRKLFi0iKioKs9nMxIkT+fDDD9Hr9UyfPp358+fz/fffo1KpePDBB9m0aROff/45r732WpO4u3fvRqc79+yEF2KxWHB3d+/QZ+uMGBJH4nSFOF2pLhLHsXHKy8sZNGhQi9scdkQQGBiIyWSyL+fm5hIQEABASkoK4eHh9ilYBw8ezMGDB9mzZw9/+ctfANt8GwsWLGgxdkcmperopFadFUPiSJyuEKcr1UXiODbO7t27z7nNYecIRo4cybp16wDbHOGBgYH2exqEhoaSkpKCxWIB4ODBg3Tv3p3IyEj27dsHwP79+y/6ZdZCCOGMHHZEMHDgQOLj40lMTESlUjF//nzWrFmDwWBgzJgxzJw5k2nTpuHi4sKAAQMYPHgwERERzJ07l2+//RaAuXPnOqp6Qggh6jj0OoI5c+Y0WY6NjbW/TkxMJDExscn2wMBAli9f7sgqCSGEOItcWSyEEE5OEoEQQjg5SQRCCOHkJBEIIYSTk0QghBBOThKBEEI4OUkEQgjh5CQRCCGEk5NEIIQQTk4SgRBCODlJBEII4eQkEQghhJOTRCCEEE5OEoEQQjg5SQRCCOHkJBEIIYSTk0QghBBOThKBEEI4OUkEQgjh5CQRCCGEk5NEIIQQTk4SgRBCODlJBEII4eQkEQghhJOTRCCEEE5OEoEQQjg5SQRCCOHkJBEIIYSTk0QghBBOztWRwRctWsS+fftQqVQkJSXRt29f+7aPPvqItWvXolar6d27N3PnzmXZsmVs2bIFAKvVislkYt26dY6sohBCOD2HJYIdO3aQnp7OqlWrSElJISkpiVWrVgFgNpt5++23Wb9+Pa6ursyYMYO9e/fy4IMP8uCDDwLwxRdfkJ+f76jqCSGEqOOwrqGtW7eSkJAAQExMDMXFxZjNZgA0Gg0ajYby8nJqamqoqKjA29vbXrampoaPP/6YKVOmOKp6Qggh6jjsiMBkMhEfH29fNhqN5OXlodfrcXNz46GHHiIhIQE3NzduvfVWoqKi7O9dv349o0aNwt3dvcXYycnJ7a6XxWLpUPnOiiFxJE5XiNOV6iJxLl6cZhQHeeqpp5TvvvvOvpyYmKikpqYqiqIopaWlyrhx45T8/HylsrJSSUxMVJKTk+3vnTFjhpKRkdFi3F27dnWoXocPH+5Q+c6KIXEkTleI05XqInEcG+d8bafDuoYCAwMxmUz25dzcXAICAgBISUkhPDwco9GIVqtl8ODBHDx4EIDy8nLOnDlDWFiYo6omhBCiEYclgpEjR9pH/Bw6dIjAwED0ej0AoaGhpKSkYLFYADh48CDdu3cH4MiRI0RHRzuqWkIIIc7isHMEAwcOJD4+nsTERFQqFfPnz2fNmjUYDAbGjBnDzJkzmTZtGi4uLgwYMIDBgwcDkJeXh9FodFS1hBBCnMWh1xHMmTOnyXJsbKz9dWJiIomJic3K3Hzzzdx8882OrJYQQohG5MpiIYRwcpIIhHBm1tpLXQPRBTi0a0gIcQnUVII51/YoywVzTsNy/esy23JsVRlsGQgxN8FVN0HoYHCRZsHZyF9ciMuFokDpGSg8CYVp+KUegDTF1riX5dU18jlgKW65vLsP6INAHwghA0AfRH5RKf7mI/DTEvjxeXDzhujrGhKDT8TF/YzikpBEIERXUlsDxRm2xr4gFQpO2h6Fdc81Ffa3BgJoDaAPsDXwgXEQdX1DY29/BIFnALi6NftxecnJ+MfFQXkBnNwMJzZCyveQ/B/bG/yuakgK3UeB1vPi/B7ERSWJQIiLTFVbCXlHGzX0qQ0Nf9EpsNY0vNnVHXy7g28URI8GY5Tt4RvFkexiYvsM7JxK6YwQf4ftoSi2+qVstCWGPe/DjjfBRQsRwxoSQ1BvUKk65+eLS0oSgRCOZK2FnEOQsR1ObYPMHfQqygCUhve4edka9+C+cPXv6hr7aFvjb+gG6pbHdCi5DphzBmyNe2Cs7TH8IaiugPQttiOFlO9hw3zbQx8EMTdCzE241IY6pi7iopBEIERnqiyFzF2NGv5dUFVq22boBuHXYAq7mYCeQxsae52xa+9ZazxsRwBX3WRbLsm2JYQTG+HYt7DvY3oC/NITwq+xHTWEDwO/mK79uYSdJAIhOqI4C05tbWj4cw6CYgVUEBQPfSfUNYzX2E68qlSYkpMJiIu71DVvP68QGDDF9rDWQvZecrd/SqAlFZLXwq8f2t6n82/47BHDoFu/Fs9TdBnWWijPbzLKyjs7G3RFtqStD7xiE5skAiFay1oLp/c3NPoZ220ndgE0nhA2CK6dAxHXQNgQcPc+f7wrgdoFwgaRX6ojMC4OrFYwHW34/ZzaBkf+a3uvixuEDqxLDMMhfKjtaMiRFAUqCpsPoS1rPJy2bsRVuakuiTcIAdhRt6DxrDs/071p950xGrzDbL+Ly5QkgvZSFKixQFUZVJnrns9+NFpf3XxbZEkBbNF1uCoRVTVwPP6sL2cUuBk64YN2cfa9uOZj5OuXI0sLO/57VmrpeeYQ1JTblg3dbHu5w2fZGv6gPjL+HmznMwLjbI/Bf7CtK82xJYWM7bajp62vwS//sm3z72X7/YUPs/0+FcX2qC5v8/8nqsrtr7sX5cDXJbZhtdbq5vV0cWsYVeUTbktQTUZb2V6fOHGcq3xdmp7QNx2H499BbWWjz60B38iG/3uNk4RvpO09tTV19S5v+nmqy89qQ8zneE8Z+rDx4ICjSfnmnou1FoozG4bt2b8IJ+lZkGZrEM7aezgvjc429E7raduz0HqiqLWdcqisLiuFI/+z7dE05hnQ9AvZ+Ava1fulK81oi09Cam7TMfL1e2/1DX5ZXst/B43O9h/aMxDFpXN+zyWRv8G33y1NunlEKxiC4OrbbA+wNXLZexqOGg5/BXs+AKCXWlvXcCvnjne2uv9PtocetJ7UuvtC0OAWhtHWvXb3btXfr9pQCT1aaHitVijNbj7qq+Ck7XPVnxcCQEUvF23TxHEhKhf7Z7E9dLZllWOOOpw7EdRU2obrtTSMrzC96Z6Ei5v9kLDYEIsxJKrJF8/W0Df+wzV6aHQtHjaeSk4mrhOye1p9HEtJi4mLtJ9h/ydNC7l5g7F7k0ShK1VDqDfog885UqXTKYot4eYchDMHbI+cg1BwkpizGwMXbcNem3dYy3txnnVj6t309mKd9Xs+k5yM7+Xct99VaHW2axK6j7ItN+pOKjy+A7+gsGYNu70hPHu9q0eL39WMTvqbn5NabfsOeodB1LVNtymK7Si1UbtSeCYDv26R5/8sjdsQV7cWE5XZEXcnw5kSgdVK5a63CUr+CXYUQkFaXf9uo8ZGa7A1jkHxEPvbpnvRhhD7Fy4nORljV2wQ3L1sJ+S69Wu+rdoCRenNk97pfbaLh6w1RAJsom7sesN4dfvYdWM0eEe0vwukphJyk+sa/YMNjb+lqOE9xmjb+PR+k8iyuBPac0BDY9/KvThxmWnUnZTrOQy/rvh/qy1UKvD0tz3ChwKQm5zcpT+X0ySCgqKTjD38CmoFIlUaugf60b17byJ9YogK7EtkyFD0PpGXVUNTXVtNXkUeJ8tOYs23ggIKCoqiYP+n2BKdfb1PIIp3AErUENt6RUGx1qCYcylIPUiM3gXv0jy8irLwKDyJKmVTk6tZUbnYukXO7getP4mm8bC9z5wHOQdsDX79Xr7pWMPFUhodBF5tu4ApuLetjz3o6ibnNUqSkwmN6rr/ecSlV2OtIbc8l2xzNqfLTnO67DTZ5mzO5J8hqjQKfw9//Dz88Pfwtz983XxxuYxP7DqC0yQCX99oFlz7HD8e/4kS1xIOFqexvvQw1pKDcOorAPw9/Onu1Z3u3t1tz3WvQ/WhuKov3q9KURSKK4vJKc8hryKP3PJccspzyC3PJbc8l7zyPHLKcyiwFDQUOtRJPzy34aWr3hUvv554uXripdJgALxqavCqtuBlycQr5QBeVeV4Wa0ND48AQqoqUSrysadUr1DbXn6vW2zPwX1tiUP+M14yVbVVpBansq1gG7WmWrp7dUev1V+44EVWXl3OmbIzZJfVNfTm07bXdc+55blYzzpHZHQ3olE07D2xl7LqsmYxVajwdfe1JwY/d79mCaN+Xf2O1JXOaRKBSqXi1pjfEl0VY+87rKqtIqM0g7TiNE6WnCS9JJ204jQ2pG+gqLKhu8JV5UqYIcyeIFzNriS7JqOqa+pUKhUqVPbnJusar1fRZF16fjo7Du1o0rjnlueSV5FHZQsnlozuRgJ1gQR4BHC139UE6YII1AViNpmJCItoUg+gyc9u8vPPqlu91LRUfIJ9KKkqoaSyhNKqUtvruuWiqhJOUUKJtYpSTRVWbw/Ao4XftisalQFfjQEfD398PfzxdvPG112NT3U2vnkV+JQcxdfNF293b3zdfPFx88HD1aNJfa4UtdZaMkozOFZ4jNTiVCoKK8j3yifcEE6IPgSNWuPQn19oKeRo4VGOFtQ9Co+SWpRKjVJ3dHbC9hSoCyTaO5oo7yiivaPtr/09/B3yd6m11mKqMHGm/AxnymyPwxmHqTxTadurLztDYWVhkzIuKheCPYMJ9gxmSNAQuum7EeIZQjfPbnTTd6ObZzfcXd1JrjtHUF5dTr4ln/yKfEwVJtuzxYSpwmRfPll8kvyKfKqsVc3qqFVriTgeQbghnAhDBBFeEYQZwogwRBDsGXxRdxAd6cr4FO2kddES4xNDjE9Ms21FliLSStJIK0mzJ4i0kjR+yfqFams1pHdePdxc3AjUBRKoC6RPQB97A9/4EeARgNZF22L55Npk4iI63oWiL9QT1711cRRFoay6rEmiKKmyJY9jGcfQemspqiyi0FJIUWURxwqPUVxZTFFlEco5RoS4ubjh4+aDr7stMdRU1KA/3Ql7qRUQXxVPmCGMMH0YYYYw/Nz9HNK4FVgKOFZ4jOOFx+3PKUUpWGotTd73Tvo7gK1h6+bZjQgvW2PTuMEJ1Yfi7ure6p9tVaxklGZwpOCIvcE/UnCE3PKGw7xAj0B6GXtxXdh19DL2otZUi3ugOyeLT3Ky+CSpRamsTVnbZE/aoDEQ5RNFlFcU0T4NCeJ8R8pWxUqBpcDewJ8pO0NOeU7DcvkZ8srzqFWa3g/BTe1GmCGMYH0wvf17E6Kva+Q9uxGiDyHAI6BN3To6jQ6dRke4Ify871MUhdLq0oZkUfd88NRByrRlnCo5xZbsLU120FzVrvbvU/3frP5vGKYPQ+PS+gRfY62hrLqM0qpS+7O52mx/NleZKasuo6fSkzhk+OhF4+PuQ3/3/vQP7N9kfa21lm0HthEdE92kH16xddA3W9dk/Vl996fSTjE0fiheWq/Lbk9YpVKh1+rRa/WE2C67sUuuOfeIjVprLaVVpRRWFjZJFEWVRRRZimzrLUUUVBZQUlWCuczcoXoqikKuOZcfTD80We/h6kGoPpRQfWiTBFG/Tqc5/3UHlbWVpBalNm30i45jqmgYwmt0N9LTtycTek2gh28Pevr2JNo7mj2H9uAe7M6p0lOcKjlFZmkmp0pPccB0gNImww4hSBfULEmEG8IJ8gziuPk4B44esDf6xwqPUVF3PsdF5UKUdxRDgocQ6xtLL2Mvehl7YXRvegFXckXznQhFUcgtzyW1OJXU4lR7kvgl+xe+SvnK/j6NWkOkVyRR3lFoLVqUPMXe4OeU51DTePI8bIk+2DOYIF0QQ4OHEqQLsu/d17/OSsni6quvPv8f1QFUKhVeWi+8tF5Ee0fb1yerGr7LVsVKbnkuGaUZnCo5ZXsutT3vydlDef01JoBapaabZzf7362ipAK3QreGhr66FHOVrYEvrS61/93OR6PWMD18eud/eCQRtJmL2gWj1kg3fbcOx7LmWPF2c4KrTxtxUbvg4+6Dj7vPBd+b3ElDAJOTk4nqEUW2OZtMcyaZpZlNnnec2dHsP6Kfu589MYQZwgjxDOFI9hGKcm1HN+kl6fa9Wa3admQ5MmSkvcHv4dsDfw//Fuvjq/UlLiiOgUHNZw4trizmVMkpW5IorUsSJaf4IeOHpueEGtFr9PQy9uKOq+4g1mhr9GN8YnBzad+1EyqViiDPIII8gxgeMrzJtpKqEvuRw8mSk5wsOsnRgqPklOUQoAsgyDOI/oH9CdY1beCDPYPxcfO54A5Ptiq7XXW+GNQqdUO3VPCQJtsURaHAUtAkOdQni/Xp67FUW/Aq9rLtPGlsjyBdEAatwbas1WPQGPDUeNrW1S3Xv9+gNaB10ZIsw0eFaD93V3dbt4ZPdLNtiqJQWFloSwylmWSZs+yJYl/ePr5N+9Z+QjJUH0oP3x7cFHETPY096enbkwhDRKf1FXu7edMnoA99Avo021ZWXWZvYM6UnUEpVripz02E6kMv2hGll9aLfgH96BfQdIhyZyXty5VKpcLPww8/D79mvQjQ9X8/kgiE01OpVBjdjRjdjfQN6Ntse7W1mpyyHHLScxjUe9AlqKGNp8aTWGMsscZYwNa4hBnCLll9xJVDbl4vxAVo1BrCDGHoXDo+L5QQXZEkAiGEcHKSCIQQwslJIhBCCCcniUAIIZycJAIhhHBykgiEEMLJSSIQQggnp1Ius3lWd+/efamrIIQQl6VBg1q+IPKySwRCCCE6l3QNCSGEk5NEIIQQTs5pEsHzzz/PxIkTueuuu1i/fn27YlRUVPDII48wZcoU7rnnHjZt2tShOlksFhISElizZk27ym/fvp1hw4YxdepUpk6dyjPPPNPuuqxdu5bbbruNO++8kx9++KFdMT777DN7XaZOncqAAQPaFaesrIxZs2YxdepUEhMT+emnn9oVx2q1Mm/ePBITE5k6dSopKSltKn/s2DESEhJYsWIFAKdPn2bq1KlMnjyZRx55hKqq5ne0ak0cgA8++ID4+HjKyprfSrEt9bn33nuZMmUK9957L3l5ee2K8+uvvzJp0iSmTp3KzJkzKShoebrrC30mgJ9++olevXq1+zM98cQTjB8/3v4dau138ew41dXV/PWvf+Xuu+9m+vTpFBcXtyvOww8/bK/L+PHjmTdvXrvi7Ny50/47/r//+7921yclJYXf//73TJkyhaeeeoqampoLRGgdp5h9dNu2bRw/fpxVq1ZRWFjIHXfcwdixY9scZ9OmTfTu3Zv77ruPrKwsZsyYwejRo9tdr2XLluHt3bH7EQwdOpRXX321QzEKCwt5/fXX+fzzzykvL2fp0qXccMMNbY5zzz33cM899wCwY8cOvvnmm3bV54svviAqKoq//vWv5OTkMH36dL799ts2x9m4cSOlpaV88sknnDp1ioULF/Lmm2+2qmx5eTnPPPMMw4c3zMf/6quvMnnyZG655RZeeuklVq9ezeTJk9sc58svvyQ/P5/AwMBWf5aW4vzrX/9iwoQJjBs3jo8++oh3332Xv/3tb22O8+677/L8888THh7Oa6+9xqeffsoDDzzQphgAlZWVvPXWWwQEBLT7MwE8+uijbfp/1VKcTz/9FF9fX1588UVWrVrFrl27uOmmm9ocp/H/rSeffNL+/W5rnGeffZYlS5YQHR3NG2+8wapVq7j//vvbHGfJkiXcf//9XH/99bz++ut88803jB8//oJ1uhCnOCIYMmQIr7zyCgBeXl5UVFRQW1t7gVLNjRs3jvvuuw+w7Y0FBQW1u04pKSmcOHGiXQ1uZ9u6dSvDhw9Hr9cTGBjYoSOLeq+//jp/+tOf2lXW19eXoiLbPaNLSkrw9fVtV5y0tDT69rVNKx0REUF2dnar/+5arZbly5c3aay3b99ub0xGjx7N1q1b2xUnISGB2bNnt+keAi3FmT9/PjfffDPQ9HfW1jivvvoq4eHhKIpCTk4OwcHBbY4B8MYbbzB58mS02pZvqdraOG3VUpxNmzZx2223ATBx4sQLJoEL1Sc1NZXS0lL796mtcRr/fYqLi1v1nW4pTnp6ur0O1157Lb/88ssF47SGUyQCFxcXdDrbFMKrV6/muuuuw8Wl9fc9PVtiYiJz5swhKSmp3TEWL17ME0880e7y9U6cOMEDDzzApEmT2v2lyMzMxGKx8MADDzB58uRWNXDns3//frp169bqPcOz3XrrrWRnZzNmzBimTJnC448/3q44PXv25Oeff6a2tpbU1FQyMjIoLCy8cEHA1dUVd/em9wuuqKiwN3J+fn6t6oppKY5e3/b7MLcUR6fT4eLiQm1tLStXrmzVnmFLcQB+/PFHfvOb32AymewNaFtinDx5kiNHjnDLLbe04tOcvy4rVqxg2rRpzJ49u1XdVC3FycrK4scff2Tq1KnMnj27VUnyXPUBW1felClTLhjjXHGSkpJ46KGHuPnmm9m9ezd33HFHu+L07NmTzZs3A7ZuOJPJ1FLRNnOKRFBvw4YNrF69mr///e8divPJJ5+wbNkyHnvsMdoz+vbLL7+kf//+hIef/4baF9K9e3dmzZrFsmXLWLx4MXPnzm11v/XZioqKeO2113juued48skn2/W56q1evbpVX/Rz+eqrrwgJCeG7777j/fff5+mnn25XnOuvv54+ffrw+9//nvfff5/o6OgOfa7Gusqo69raWv72t78xbNiwZl0sbXHdddfx7bffEh0dzVtvvdXm8s8++yxPPvlku39+vdtvv505c+bwwQcfEBcXx2uvvdauOIqiEBUVxYcffkiPHj1a3SXYkqqqKnbv3s2wYcPaHeOZZ57htddeY926dQwaNIiVK1e2K87jjz/ON998w7Rp02z3QO+k76HTJIKffvqJN954g+XLl2MwGNoV4+DBg5w+fRqAuLg4amtrW7XHcrYffviBjRs3MmHCBD777DP+/e9/s2XLljbHCQoKYty4cahUKiIiIvD39ycnJ6fNcfz8/BgwYACurq5ERETg6enZrs9Vb/v27e0+UQywZ88eRo0aBUBsbCy5ubnt6soDmD17Np988gkLFiygpKQEPz+/dtdLp9NhsVgAyMnJ6XCXRmd48skniYyMZNasWe2O8d133wG2O7XV77G2RU5ODqmpqcyZM4cJEyaQm5vb6r3nsw0fPtx+S8cbb7yRY8eOtSuOv78/Q4bY7is8atQoTpw40a44YDvR25ouofM5evSo/WKuESNGcPDgwXbF6datG2+++SYffPAB/fr1IzQ0tEP1qucUiaC0tJTnn3+eN998Ex+fC980/Vx27drFO++8A4DJZKK8vLxd/df/+te/+Pzzz/n000+55557+NOf/sSIESPaHGft2rW8k9T/twAABpVJREFU/fbbAOTl5ZGfn9+u8xajRo1i27ZtWK1WCgsL2/25wNYoeHp6trqfuCWRkZHs27cPsB3ie3p6tqsr78iRI/a91B9//JGrr74atbr9X/kRI0awbt06ANavX8+1117b7lidYe3atWg0Gh5++OEOxVm6dKn9puj79u0jKiqqTeWDgoLYsGEDn376KZ9++imBgYHNRhO11p///GcyMjIA2w5Fjx492hXnuuuus482O3ToUJs/U2MHDhwgNja23eXBlpjqk9GBAweIjIxsV5xXX33VPpJqzZo13HjjjR2qVz2nuLJ41apVLF26tMmXYfHixYSEhLQpjsViYe7cuZw+fRqLxcKsWbM6/IdYunQpoaGh3HnnnW0uazabmTNnDiUlJVRXVzNr1iyuv/76dtXjk08+YfXq1QA8+OCDrTq51pKDBw/yr3/9i//3//5fu8qDbfhoUlIS+fn51NTU8Mgjj7Sr28NqtZKUlMSJEydwc3NjyZIldOvWrVVlDx48yOLFi8nKysLV1ZWgoCCWLFnCE088QWVlJSEhITz77LNoNJo2xxkxYgRbtmxh79699OnTh/79+19wtE9LcfLz83Fzc7Ofc4iJieEf//hHm+M89thjLFq0CBcXF9zd3Xn++efPe+TUUoylS5fad7JuvPFGvv/++/PW41xxpkyZwltvvYWHhwc6nY5nn332gkdx5/pbLVy4kLy8PHQ6HYsXL8bf37/NcZYuXcrSpUsZNGgQ48aNu+BnOlec2bNn8/zzz6PRaPD29mbRokV4eXm1Oc6cOXN45plnUBSFwYMHd0p3HDhJIhBCCHFuTtE1JIQQ4twkEQghhJOTRCCEEE5OEoEQQjg5SQRCCOHkJBEI0U6ZmZntGvbbHtdccw0ACxcuJCMjA7PZzM8//3xRfra48kkiEOIyMnfuXMLDwzl06FCnTTgmhFNMQy2cU3Z2No899hhqtZra2lpeeOEFvL29+etf/0p5eTkWi4V58+bRt29fEhISmDBhAt9++y2RkZHEx8fbX7/44os88cQT6HQ6UlNTKSws5Nlnn21yQdCuXbt46aWXcHV1pVu3bjzzzDNUVlbyl7/8haqqKqqqqvj73/9OfHy8vczhw4dZsGABWq0WrVbLyy+/zPvvv8+ZM2c4ffo0eXl5PPb/27t3kFT/OI7jb4OiwaALZJMIQYFD1NiFEBKFhgoiJCIaJAoqnEKDrIguoBKB1NIFwcDFAqHAlppSmgoqMBwqKiIKiqwhC/kP599DEQc6cjiDfl+TqM+FZ/Djzwc/3+FhGhsblW26u7txOp1MTk7y/PyMTqfDYrH80+sqMo8EgchY29vb1NXVMTAwwMnJCXd3dySTSTo6OjAajUSjUZaWlvB6vaRSKfR6Pb29vRgMBkwmE8FgEIPBwNPTEwDv7+/4fD52dnZYWFj48q/OqakpfD4fhYWFuFwuwuEw+fn5aDQaZmZmuLy85Ozs7Mv5bWxs0NnZSVtbG9FoVGkzvb29ZXV1ldPTU+x2+5cg+GC1WonH4xIC4q+QIBAZq76+nsHBQRKJBGazmZqaGhKJBIuLi6ysrJBMJpV6coCqqipUKhUlJSXo9XoAiouLSSQSAEofVHV1NR6PR9nu/v6ei4sLhoaGAJSuptbWVubn5xkbG8NkMn37QG9qamJiYoLz83Oam5spLy8HUOo0Kisr0yoRFOJPSRCIjFVRUUEoFGJvb4+5uTna29u5urpCo9Hgdrs5OjrC5XIp7/9cbPf58UcLSyqVUp77PFQmNzeX0tJS/H7/t3MIhULs7+8TCAQ4PDz80hJaW1tLMBhkd3cXh8Oh9A19Po4Q/4LcLBYZa2tri3g8jtFoxGazcXx8zMPDA1qtFvg1n+Lt7e3H+/uoZz44OFC+vQPKuNGPdkm/308sFiMSiRCJRGhoaMDpdH6rHl5bW+Px8ZGWlhZ6enqUBtCP48Risd8WI+bk5Py1ebVCyIpAZCydTsf4+LgyyWt0dJSXlxfsdjvhcJiuri42NzdZX1//0f5eX1/p6+vj5uYGt9v95bXp6WlGRkaU1YHFYkGtVjM8PMzy8jIqlepbXbRWq8Vms1FQUEBeXh6zs7MEAgHUajX9/f1cX1//dgqeXq/H4/FQVlaG1WpN7wIJ8T9pHxXiBxwOB2az+Y+GqqfD6/VSVFSU9mAXIdIhPw0JIUSWkxWBEEJkOVkRCCFElpMgEEKILCdBIIQQWU6CQAghspwEgRBCZDkJAiGEyHL/AdYl2DjQ2G4DAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"u8k4jLWj0P-0"},"source":["Thirdly we look at `min_samples_leaf` with other parameters are fixed."]},{"cell_type":"code","metadata":{"id":"BTLqxR__0atu","colab":{"base_uri":"https://localhost:8080/","height":276},"executionInfo":{"status":"ok","timestamp":1631983095111,"user_tz":-480,"elapsed":15982,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"b4768bc9-058c-4a5b-e890-8f5e070ee043"},"source":["sls = range(1,20,1)\n","dt = []\n","for sl in sls:\n","  DT = DecisionTreeClassifier(min_samples_leaf=sl, min_samples_split=16, max_depth=14)\n","  DT.fit(X_train, y_train)\n","  train_accuracy = DT.score(X_train, y_train)\n","  test_accuracy = DT.score(X_test, y_test)\n","  cv_accuracy = np.mean(cross_val_score(DT, X_train, y_train, cv=5, scoring='accuracy'))\n","  dt.append([sl, train_accuracy, test_accuracy, cv_accuracy])\n","dt = pd.DataFrame(data=dt,columns=['samples leaf', 'train accuracy', 'test accuracy', 'cv accuracy'])\n","results = pd.melt(dt,id_vars=['samples leaf'],var_name='type',value_name='accuracy')\n","\n","sns.lineplot(x='samples leaf', y='accuracy', hue='type', data=results)\n","plt.xticks(sls)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"tj7J0HL_0bIU"},"source":["We use `GridSearchCV` to find the optimal value of each other."]},{"cell_type":"code","metadata":{"id":"I44SLcPe0h0W","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1631983239162,"user_tz":-480,"elapsed":144052,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"0ba13e14-5549-4dfd-bd08-fbc05a7c6930"},"source":["from sklearn.model_selection import GridSearchCV\n","param_grid = {\n","    'max_depth':range(8,15,1),\n","    'min_samples_leaf':range(14,20,1),\n","    'min_samples_split':range(13,19,1)\n","}\n"," \n","grid = GridSearchCV(DecisionTreeClassifier(), param_grid, cv=5, scoring='accuracy')\n","grid.fit(X_train,y_train)\n","print(\"best_params:\", grid.best_params_)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["best_params: {'max_depth': 14, 'min_samples_leaf': 14, 'min_samples_split': 14}\n"]}]},{"cell_type":"markdown","metadata":{"id":"S8Dbx5hIeq5I"},"source":["### Evaluation"]},{"cell_type":"code","metadata":{"id":"tbOqvRWZebPx","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"ok","timestamp":1632119690544,"user_tz":-480,"elapsed":967,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"0565db24-4d3c-49a4-aaf5-5e0de9701999"},"source":["DT1 = DecisionTreeClassifier(min_samples_leaf = 14,min_samples_split = 14, max_depth =14)\n","DT1.fit(X_train,y_train)\n","y1_predict = DT1.predict(X_test)\n","mccs.append(evaluation(y_test,y1_predict))"],"execution_count":37,"outputs":[{"output_type":"stream","name":"stdout","text":["TN,FP,FN,TP: 3640 346 252 284\n","mcc value: 0.4135652938837087\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"D1RU0loseKjR","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"ok","timestamp":1632119700830,"user_tz":-480,"elapsed":991,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"c32c98e6-c785-4bc3-f12f-55c894dc2dc4"},"source":["DT2 = DecisionTreeClassifier(min_samples_leaf = 14,min_samples_split = 14, max_depth =14)\n","DT2.fit(X_train_all_feature,y_train_all_feature)\n","y2_predict = DT2.predict(X_test_all_feature)\n","mccs_a.append(evaluation(y_test,y2_predict))"],"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["TN,FP,FN,TP: 3738 248 272 264\n","mcc value: 0.43897011405001973\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"U3IozDUJXnRP","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"ok","timestamp":1632119705946,"user_tz":-480,"elapsed":1189,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"d293e80f-4103-4832-d0d3-d4bd1c24e6ea"},"source":["DT3 = DecisionTreeClassifier(min_samples_leaf = 14,min_samples_split = 14, max_depth =14)\n","DT3.fit(X_g_train,y_g_train)\n","y3_predict = DT3.predict(X_g_test)\n","mccs_g.append(evaluation(y_g_test,y3_predict))"],"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["TN,FP,FN,TP: 3786 190 281 265\n","mcc value: 0.4739315204675663\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"QIOw7lCyFr7F"},"source":["\n","## 6.4 Random Forest"]},{"cell_type":"code","metadata":{"id":"_5eVL4Hl3zR_","executionInfo":{"status":"ok","timestamp":1632119717701,"user_tz":-480,"elapsed":325,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["from sklearn.ensemble import RandomForestClassifier"],"execution_count":40,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"MMm34vxNYA-d"},"source":["First, tune the `max_depth` parameter. "]},{"cell_type":"code","metadata":{"id":"WaTc17BI7BRy"},"source":["rf = []\n","for depth in range(2, 21, 2):\n","    RF = RandomForestClassifier(n_estimators=100, max_depth=depth)\n","    RF.fit(X_train, y_train)\n","    train_accuracy = RF.score(X_train, y_train)\n","    test_accuracy = RF.score(X_test, y_test)\n","    cv_accuracy = np.mean(cross_val_score(RF, X_train, y_train, cv=5))\n","    rf.append([depth, train_accuracy, test_accuracy, cv_accuracy])\n","rf = pd.DataFrame(data=rf,columns=['depth', 'train accuracy', 'test accuracy', 'cv accuracy'])\n","results = pd.melt(rf,id_vars=['depth'],var_name='type',value_name='accuracy')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8FWTI9NpDdjs","colab":{"base_uri":"https://localhost:8080/","height":293},"executionInfo":{"status":"ok","timestamp":1631983389349,"user_tz":-480,"elapsed":739,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"370de31c-cc3f-4e6e-d341-3c16849ecf82"},"source":["sns.lineplot(x='depth', y='accuracy', hue='type', data=results)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f03601551d0>"]},"metadata":{},"execution_count":62},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"Z_wgeQV0YkMg"},"source":["### Best `max_depth`\n"]},{"cell_type":"markdown","metadata":{"id":"ZagR_z7Lg_W8"},"source":["Choose the value of max_depth parameter when test accuracy gets the best. Then tune the n_estimators parameter."]},{"cell_type":"code","metadata":{"id":"GeF7rHgZoUjd"},"source":["rf = []\n","for e in range(20, 141, 20):\n","  RF = RandomForestClassifier(n_estimators=e, max_depth=16)\n","  RF.fit(X_train, y_train)\n","  train_accuracy = RF.score(X_train, y_train)\n","  test_accuracy = RF.score(X_test, y_test)\n","  cv_accuracy = np.mean(cross_val_score(RF, X_train, y_train, cv=5))\n","  rf.append([e, train_accuracy, test_accuracy, cv_accuracy])\n","rf = pd.DataFrame(data=rf,columns=['estimators', 'train accuracy', 'test accuracy', 'cv accuracy'])\n","results = pd.melt(rf,id_vars=['estimators'],var_name='type',value_name='accuracy')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rWKrWy1_pV9a","colab":{"base_uri":"https://localhost:8080/","height":293},"executionInfo":{"status":"ok","timestamp":1631983497101,"user_tz":-480,"elapsed":704,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"dcb184b1-00ce-4c1b-a570-d4919420e6dd"},"source":["sns.lineplot(x='estimators', y='accuracy', hue='type', data=results)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f03600e1f10>"]},"metadata":{},"execution_count":64},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"0PvLANRPjTig"},"source":["### Optimize parameters with `GridSerchCVOptimize`"]},{"cell_type":"markdown","metadata":{"id":"V_ZHXEArZE7y"},"source":["Make sure the range of each parameters tested before， using GridSerchCV to find the best parameters for this model."]},{"cell_type":"code","metadata":{"id":"sudqieXUKXgR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1631983910103,"user_tz":-480,"elapsed":413016,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"22c6f744-f9c5-4da7-ff36-4baddb4bc721"},"source":["param_grid = {\n","    'max_depth': [i for i in range(14,19,1)],\n","    'criterion': ['gini', 'entropy'],\n","    'n_estimators': [50, 60, 70, 80],\n","}\n","rf_grid = GridSearchCV(estimator=RandomForestClassifier(), param_grid=param_grid, cv=5)\n","rf_grid.fit(X_train, y_train)\n","print(\"best_params:\", rf_grid.best_params_)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["best_params: {'criterion': 'gini', 'max_depth': 18, 'n_estimators': 80}\n"]}]},{"cell_type":"code","metadata":{"id":"Qr2fnI7BWFfb","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"ok","timestamp":1632119731566,"user_tz":-480,"elapsed":5385,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"f63f7258-82ea-4cd2-9128-a124eeaea7d6"},"source":["RF1 = RandomForestClassifier(n_estimators=80, max_depth=18, criterion=\"entropy\")\n","RF1.fit(X_train,y_train)\n","y1_predict = RF1.predict(X_test)\n","mccs.append(evaluation(y_test,y1_predict))"],"execution_count":41,"outputs":[{"output_type":"stream","name":"stdout","text":["TN,FP,FN,TP: 3701 285 223 313\n","mcc value: 0.4889806378052381\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"5YSznD2PeYKK","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"ok","timestamp":1632119740627,"user_tz":-480,"elapsed":7017,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"2770c889-f6d7-4186-a01a-6622b41c265a"},"source":["RF2 = RandomForestClassifier(n_estimators=80, max_depth=18, criterion=\"entropy\")\n","RF2.fit(X_train_all_feature,y_train_all_feature)\n","y2_predict = RF2.predict(X_test_all_feature)\n","mccs_a.append(evaluation(y_test,y2_predict))"],"execution_count":42,"outputs":[{"output_type":"stream","name":"stdout","text":["TN,FP,FN,TP: 3849 137 304 232\n","mcc value: 0.47048701921394176\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"UVeNIT9PZU4v","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"ok","timestamp":1632119752978,"user_tz":-480,"elapsed":9601,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"21ad570e-07c3-4249-9657-7919da22bc60"},"source":["RF3 = RandomForestClassifier(n_estimators=80, max_depth=18, criterion=\"entropy\")\n","RF3.fit(X_g_train,y_g_train)\n","y3_predict = RF3.predict(X_g_test)\n","mccs_g.append(evaluation(y_g_test,y3_predict))"],"execution_count":43,"outputs":[{"output_type":"stream","name":"stdout","text":["TN,FP,FN,TP: 3815 161 274 272\n","mcc value: 0.5067861164878721\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"K15LzthVFuge"},"source":["## 6.5 AdaBoost"]},{"cell_type":"code","metadata":{"id":"27IjuOcdEAB9","executionInfo":{"status":"ok","timestamp":1632119756687,"user_tz":-480,"elapsed":297,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["from sklearn.ensemble import AdaBoostClassifier"],"execution_count":44,"outputs":[]},{"cell_type":"code","metadata":{"id":"8cMPhCQgzDr-","colab":{"base_uri":"https://localhost:8080/","height":293},"executionInfo":{"status":"ok","timestamp":1631984476631,"user_tz":-480,"elapsed":549075,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"c4532091-944e-43f7-fa3d-2d430dfe4536"},"source":["ab = []\n","for e in range(50, 550, 50):\n","  AB = AdaBoostClassifier(n_estimators=e)\n","  AB.fit(X_train, y_train)\n","  train_accuracy = AB.score(X_train, y_train)\n","  test_accuracy = AB.score(X_test, y_test)\n","  cv_accuracy = np.mean(cross_val_score(AB, X_train, y_train, cv=5))\n","  ab.append([e, train_accuracy, test_accuracy, cv_accuracy])\n","ab = pd.DataFrame(data=ab,columns=['estimators', 'train accuracy', 'test accuracy', 'cv accuracy'])\n","results = pd.melt(ab,id_vars=['estimators'],var_name='type',value_name='accuracy')\n","\n","sns.lineplot(x='estimators', y='accuracy', hue='type', data=results)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f03701edd50>"]},"metadata":{},"execution_count":70},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"80WXzQnE38VX","colab":{"base_uri":"https://localhost:8080/","height":293},"executionInfo":{"status":"ok","timestamp":1631984655169,"user_tz":-480,"elapsed":178123,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"1e078247-1e0c-43c1-eb66-052d0ca6a7df"},"source":["ab = []\n","for lr in [0.001,0.01,0.05,0.1,0.5,1]:\n","  AB = AdaBoostClassifier(n_estimators=150, learning_rate=lr)\n","  AB.fit(X_train, y_train)\n","  train_accuracy = AB.score(X_train, y_train)\n","  test_accuracy = AB.score(X_test, y_test)\n","  cv_accuracy = np.mean(cross_val_score(AB, X_train, y_train, cv=5))\n","  ab.append([lr, train_accuracy, test_accuracy, cv_accuracy])\n","ab = pd.DataFrame(data=ab,columns=['learning rate', 'train accuracy', 'test accuracy', 'cv accuracy'])\n","results = pd.melt(ab,id_vars=['learning rate'],var_name='type',value_name='accuracy')\n","\n","sns.lineplot(x='learning rate', y='accuracy', hue='type', data=results)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f03704d3110>"]},"metadata":{},"execution_count":71},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"PXjLTHWJ5edo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1631985119125,"user_tz":-480,"elapsed":463965,"user":{"displayName":"QIAODAN JU","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08754974744610280635"}},"outputId":"6864e65d-8dd1-4266-cedd-a28e0f15bcd0"},"source":["from sklearn.model_selection import GridSearchCV\n","param_grid = {\n","    'learning_rate': [0.6,0.8,1.0,1.2],\n","    'n_estimators': [150, 175, 200, 225],\n","}\n","ab_grid = GridSearchCV(estimator=AdaBoostClassifier(), param_grid=param_grid, cv=5)\n","ab_grid.fit(X_train, y_train)\n","print(\"best_params:\", ab_grid.best_params_)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["best_params: {'learning_rate': 1.2, 'n_estimators': 150}\n"]}]},{"cell_type":"code","metadata":{"id":"Bb8HUCSx-OJr","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"ok","timestamp":1632119774655,"user_tz":-480,"elapsed":7869,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"b6014fd8-54c2-4a77-c720-9ff2f27e0b73"},"source":["AB1 = AdaBoostClassifier(n_estimators=150, learning_rate=1.2)\n","AB1.fit(X_train,y_train)\n","y1_predict = AB1.predict(X_test)\n","mccs.append(evaluation(y_test,y1_predict))"],"execution_count":45,"outputs":[{"output_type":"stream","name":"stdout","text":["TN,FP,FN,TP: 3701 285 228 308\n","mcc value: 0.48179243057586957\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"bg85Q4Q1e0l2","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"ok","timestamp":1632119790598,"user_tz":-480,"elapsed":14114,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"f335d0c5-7f70-4ee8-90d0-e1a97818e9a0"},"source":["AB2 = AdaBoostClassifier(n_estimators=150, learning_rate=1.2)\n","AB2.fit(X_train_all_feature,y_train_all_feature)\n","y2_predict = AB2.predict(X_test_all_feature)\n","mccs_a.append(evaluation(y_test,y2_predict))"],"execution_count":46,"outputs":[{"output_type":"stream","name":"stdout","text":["TN,FP,FN,TP: 3852 134 313 223\n","mcc value: 0.4584142480262741\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"zvyRGj_rZrpj","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"ok","timestamp":1632119809605,"user_tz":-480,"elapsed":16719,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"7f2db4c0-534a-495b-d057-3a0ee734b9f3"},"source":["AB3 = AdaBoostClassifier(n_estimators=150, learning_rate=1.2)\n","AB3.fit(X_g_train,y_g_train)\n","y3_predict = AB3.predict(X_g_test)\n","mccs_g.append(evaluation(y_g_test,y3_predict))"],"execution_count":47,"outputs":[{"output_type":"stream","name":"stdout","text":["TN,FP,FN,TP: 3823 153 323 223\n","mcc value: 0.43656302799029784\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"r7FmOnf8CRHI"},"source":["# 7.Summary of Achievements"]},{"cell_type":"markdown","metadata":{"id":"ClMBOL_clePa"},"source":["## 7.1 Mcc comparison"]},{"cell_type":"code","metadata":{"id":"olqsb7OQjhdZ","colab":{"base_uri":"https://localhost:8080/","height":279},"executionInfo":{"status":"ok","timestamp":1632119822201,"user_tz":-480,"elapsed":526,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"65f5ec54-ebd5-4c19-f253-483f80dac696"},"source":["plt.plot(models,mccs, label='selected features')\n","plt.plot(models,mccs_g, label='generated features')\n","plt.plot(models,mccs_a, label='all features')\n","plt.xlabel('model')\n","plt.ylabel('mcc')\n","plt.legend()\n","plt.show()"],"execution_count":48,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"KmGMuLgq_Xo5"},"source":["The data_set with generated features performs better, which means that the intersection of features brings additional information, such as duration_cellular. The data performance after feature selection is also better than the original data, which means that this removes some irrelevant features and reduces the complexity of the model."]},{"cell_type":"markdown","metadata":{"id":"3bx-resRrReJ"},"source":["## 7.2 Feature Importance"]},{"cell_type":"code","metadata":{"id":"Lr_LIqhswibz","executionInfo":{"status":"ok","timestamp":1632120260686,"user_tz":-480,"elapsed":344,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}}},"source":["def show_feature_importance(features_list,model):\n","  importances=model.feature_importances_\n","  x_values = list(range(len(importances)))\n","  plt.barh(x_values, importances)\n","  plt.yticks(x_values, features_list, fontsize=9)"],"execution_count":49,"outputs":[]},{"cell_type":"code","metadata":{"id":"-xWY-Sgbwmvc","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1632120272472,"user_tz":-480,"elapsed":4676,"user":{"displayName":"马乙玮","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhQpt0Ddzm0h-dpnUp5bM9KqHrr9Fq18oUgLP8y=s64","userId":"03621358809911246122"}},"outputId":"4b29e087-4ed0-458d-cb5c-77959b66a80a"},"source":["tree_models = [DT1,RF1,AB1,DT2,RF2,AB2,DT3,RF3,AB3]\n","features_lists=[bestKFs[:22],bestKFs[:22],bestKFs[:22],train_set.columns,train_set.columns,train_set.columns,bestKFs_g[:32],bestKFs_g[:32],bestKFs_g[:32]]\n","ylabels=['selected_features','selected_features','selected_features','all_features','all_features','all_features','generated_features','generated_features','generated_features']\n","xlabels=['DT','RF','AB','DT','RF','AB','DT','RF','AB']\n","\n","plt.figure(figsize=(12,18))\n","for i in range(1,10):\n","  plt.subplot(3,3,i)\n","  show_feature_importance(features_lists[i-1],tree_models[i-1])\n","  plt.xlabel(xlabels[i-1])\n","  plt.ylabel(ylabels[i-1])\n","plt.tight_layout()"],"execution_count":50,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x1296 with 9 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"vyzD81WK39mk"},"source":["## 7.3 Business advice"]},{"cell_type":"markdown","metadata":{"id":"9hcIG6vA4Gw2"},"source":["\n","\n","\n","\n","\n","1.   Duration is very important. People with longer duration are more likely to deposit. In practice, it may be more accurate when combined with cellular.\n","2.   Housing is equally important. People with houses are more likely to deposit.\n","3.   pdays, previous, poutcome are more important, so people's history behavior should also be referenced.\n","4.   People are more likely to deposit in certain months, such as August, September, and December.\n","5.   In addition, compaign, blue collar, college degree, marriage and loan status are all relatively important and should be collected.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"tOnCBySqDvWU"},"source":["# 8.Future Directions for further Improvements"]},{"cell_type":"markdown","metadata":{"id":"wyYhx46I4Jsy"},"source":["1.   Make feature selection more precise, and intersect some features that can bring more additional information.\n","2.   Remove some outliers in the origin date.\n","3.   Tune the model parameters to be more optimal.\n","4.   Use other more complex models.\n","\n"]}]}